Research note / Note de recherche

Time series analysis of subsidence at Tauharaand Ohaaki geothermal fields, New Zealand,

observed by ALOS PALSAR interferometry during2007–2009

Sergey Samsonov and Kristy Tiampo

Abstract. We present a modification of the Small Baseline Subset (SBAS) technique and apply it to L-band ALOS

PALSAR data for time series analysis of subsidence at Tauhara and Ohaaki geothermal fields in the Taupo Volcanic

Zone, North Island, New Zealand. We noticed that residual topographic noise presented in most ALOS PALSAR

interferograms that have been acquired with large perpendicular baselines is a significant limiting factor that

complicates their interpretation. We modified the original SBAS technique in such a way that the residual topographic

contribution is accurately estimated and removed. For testing purposes we acquired 12 ALOS PALSAR images between

13 January 2007 and 18 January 2009 and created 33 differential interferograms for use in SBAS processing. The calculated

time series of deformation at both geothermal fields show steady subsidence with rates of 5–6 cm/year that is clearly

mapped with the proposed technique. A comparison of results calculated with the proposed technique and with the original

SBAS algorithm shows that the proposed approach is more accurate and produces results of higher quality. The technique

presented in this paper can be used for time series analysis of any SAR data; however, it is most appropriate for L-band

ALOS PALSAR that acquires coherent images with large spatial baselines that also correlate with the time of acquisition.

Resume. On presente une modification de la technique SBAS (« Small Baseline Subset ») et on applique celle-ci aux donnees

ALOS PALSAR en bande L pour fin d’analyse des series chronologiques de la subsidence des champs geothermiques de

Tauhara et d’Ohaaki dans la zone volcanique de Taupo, North Island, Nouvelle-Zelande. On a observe que le bruit

topographique residuel present dans la plupart des interferogrammes de donnees ALOS PALSAR acquises avec des lignes

de reference perpendiculaires larges est un facteur limitatif important qui complique leur interpretation. On a modifie la

technique SBAS originale de maniere a ce que la contribution topographique residuelle soit estimee precisement et eliminee.

A titre d’essai, 12 images ALOS PALSAR ont ete acquises entre le 13 janvier 2007 et le 18 janvier 2009 et 33 interferogrammes

differentiels ont ete crees pour utilisation dans le traitement SBAS. Les series chronologiques calculees des deformations pour

les deux champs geothermiques montrent une subsidence constante avec des taux de 5–6 cm/annee qui peut etre clairement

cartographiee a l’aide de la technique proposee. Une comparaison des resultats calcules a l’aide de la technique proposee et avec

l’algorithme SBAS original montre que l’approche proposee est plus precise et produit des resultats d’une plus haute qualite. La

technique presentee dans cet article peut etre utilisee pour l’analyse des series chronologiques de tout type de donnees RSO;

toutefois, la technique est particulierement appropriee pour les donnees ALOS PALSAR en bande L qui acquierent des images

coherentes avec des lignes de reference spatiales larges qui sont aussi correlees avec le temps d’acquisition.

[Traduit par la Redaction]

Introduction

Synthetic aperture radar interferometry (InSAR) (Mas-

sonnet and Feigl, 1998; Rosen et al., 2000) is a valuable tool

for measuring ground deformation with high spatial resolu-

tion and high accuracy. It has been successfully used for

mapping of seismic (Massonnet et al., 1993; Wyss, 2001;

Jacobs et al., 2002) and volcanic (Massonnet et al., 1995;

Lu et al., 2000; 2002) deformation as well as anthropogenic

deformation caused by mining, oil, and groundwater extrac-

tion (Shmidt and Burgmann, 2003; Hole et al., 2007). The

standard interferometric processing steps include image

coregistration, interferogram formation, removal of earth

curvature and topographic phases, filtering, and phase

Received 14 October 2009. Accepted 21 April 2010. Published on the Web at http://pubservices.nrc-cnrc.ca/cjrs on 21 January 2011.

Sergey Samsonov.1 European Center for Geodynamics and Seismology, 19 Rue Josy Welter, L-7256 Walferdange, Luxembourg; and GNSScience, 1 Fairway Drive, Lower Hutt, New Zealand.Kristy Tiampo. Department of Earth Sciences, University of Western Ontario, London, ON N6A5B7, Canada.1Corresponding author (e-mail: ssamson@uwo.ca).

Can. J. Remote Sensing, Vol. 36, Suppl. 2, pp. S327–S334, 2010

E 2010 CASI S327

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

unwrapping. The last step, interferometric phase unwrap-

ping, can be complicated or even impossible if the level of

coherence is low. The decorrelation effect or loss of coher-

ence between two SAR images depends on land cover, tem-

poral and spatial baselines, and SAR wavelength.

Until recently most interferometric studies were based on

C-band data from the European Remote-Sensing satellites

1 and 2 (ERS-1 and ERS-2), the Environmental Satellite

(Envisat), and RADARSAT-1 and were limited to sparsely

vegetated regions. For densely vegetated environments, C-

band interferometry produced limited results due to decorr-

elation (Hole et al., 2007). Advanced processing techniques

have been developed to reduce noise and to improve spatial

coverage. A stacking algorithm (Sandwell and Price, 1998;

Lyons and Sandwell, 2003; Wright et al., 2004) that can be

used for calculation of constant deformation rates reduces

atmospheric and topographic noise but at the same time

removes temporal characteristics of a signal. This is a signifi-

cant limitation in case of the nonlinear deformation that is

often observed in volcanic regions (Lu et al., 2000; 2002).

The Permanent Scatterer (PS) approach (Ferretti et al.,

2001; 2004) limits the processing only to those pixels that

behave consistently over a prolonged period of time by set-

ting a threshold on amplitude dispersion. This technique

largely depends on the ability to select a dense network of

permanent scatterers to reduce errors in phase unwrapping

and at the same time assumes a linear model of deformation.

The Small Baseline Subset (SBAS) technique (Berardino

et al., 2002; Usai, 2003; Lanari et al., 2004) selects interfer-

ograms with small spatial and temporal baselines that are

substantially coherent. Such interferograms are easy to

unwrap, and it is possible to perform time series analysis

for pixels that are coherent above a selected threshold on

all interferograms. A nonlinear PS modification and a com-

bination of PS and SBAS techniques were also recently pro-

posed (Ferretti et al., 2000; Hooper, 2008).

The recent launch of the Advanced Land Observation Sat-

ellite (ALOS) phased array type L-band synthetic aperture

radar (PALSAR) sensor (Rosenqvist et al., 2007), a successor

of the Japanese Earth Resources Satellite 1 (JERS-1) (1992–

1998), has expanded the applicability of InSAR to vegetated

regions. Since L-band interferometry is significantly more

coherent than C-band interferometry over vegetated surfaces,

phase unwrapping is usually simplified for a variety of land-

cover conditions and also for pairs with large spatial and

temporal baselines. Thus, for time series analysis it is reas-

onable to employ the SBAS technique that utilizes un-

wrapped data, rather than the PS technique that works

with wrapped interferograms or stacking that produces only

mean deformation rates. In this work we show that the SBAS

technique applied to ALOS PALSAR data produces excel-

lent results; however, some modifications are required to the

processing scheme to improve the accuracy of the results.

In this paper we present a methodology for removing

the residual topographic noise from the ALOS PALSAR

stacking and SBAS processing. A detailed description of the

technique is presented in the next section and followed by an

example. We produced a time series analysis of subsidence at

Ohaaki and Tauhara geothermal fields located in the central

Taupo Volcanic Zone (TVZ), North Island, New Zealand.

The detailed analysis of subsidence at these geothermal fields

for the time period prior to 2005 is presented in Allis et al.

(2009) based on ground leveling results. In Hole et al. (2007),

ground subsidence during 1996–2005 at these geothermal

fields was also observed; however, the spatial coverage of

C-band InSAR was limited to urban areas because of the

signal decorelation.

Modified Small Baseline Subset algorithm

Highly coherent interferograms can be successfully un-

wrapped and used in time series analysis based on the SBAS

approach. However, these interferograms are still contami-

nated with noise from various sources. This is atmospheric

noise that can be subdivided into tropospheric and iono-

spheric components and topographic noise caused by errors

or lack of resolution in the digital elevation model (DEM)

that was used for removal of the topographic phase. If a

large number of interferograms is used in the analysis, the

atmospheric contribution is minimized with SBAS proces-

sing since it is unbiased and uncorrelated in time. On the

other hand, residual topographic noise can be reduced or

amplified depending on the satellite acquisition parameters

and their variation over time.

We have observed that ALOS PALSAR acquisition para-

meters, such as spatial baselines, are correlated in time. A

similar pattern is observed for at least some paths of the

RADARSAT-2 satellite. The absolute value of perpendic-

ular baseline increases with time almost linearly, reaching

a maximum and then resetting, repeating the same pat-

tern (Figure 1). As a result, the topographic error, which is

Figure 1. ALOS orbital drift for ascending path 325 and descend-

ing path 628 over New Zealand relative to first acquisition. It is

clear that orbital pattern, and therefore topographic noise, are

correlated with time of acquisition.

Vol. 36, Suppl. 2, 2010

S328 E 2010 CASI

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

proportional to perpendicular baseline, also behaves linearly

with time. In the example presented in this paper, the sum of

perpendicular baselines of 33 interferograms used in SBAS

processing is close to 227 000 m, which causes significant

residual topographic noise that contaminates the results

and complicates their interpretation. To correct the topo-

graphic error, we apply an algorithm that separates deforma-

tion and topographic components and uses the latter for

estimation of DEM error. This general processing scheme

consists of the following steps:

(1) Standard DInSAR analysis is performed and inter-

ferograms are unwrapped. Orbital errors are estimated

and corrected, and interferograms are shifted against

chosen reference region(s).

(2) Further processing is performed only on pixels that are

coherent on all interferograms above a chosen thresh-

old. For each pixel of each interferogram the spatial

average in a neighboring window is calculated and

removed. The removed signal is assumed to be close to

the true deformation signal contaminated with the

spatially correlated atmospheric noise, and the residual

signal is a spatially uncorrelated topographic noise.

(3) For each pixel a linear regression of residual phase

versus perpendicular baseline is performed to estimate

DEM error, and an estimated topographic error is then

removed from the original interferograms. It is possible

to run steps 2–3 recursively on the same or varying size

window until conversion is achieved (DEM error

converges to zero).

(4) SVD decomposition is applied to the corrected data and

velocities are produced. The total deformation phase is

reconstructed by integration as in Kwoun et al. (2006).

The adjustment of the interferogram against one or more

chosen reference regions, as recommended in step 1, is neces-

sary for L-band data because of its sensitivity to atmospheric

and, in particular, to long wave-length ionospheric perturba-

tions. In practice this procedure is easy to perform assuming

that deformation far away from the region of interest is close

to zero. The separation of deformation and atmospheric and

topographic components before the regression analysis is

necessary because of the time dependence of the perpendic-

ular baseline. The size of the spatial window should be larger

than correlated topographic features. We noticed that spa-

tially uncorrelated topographic noise caused by random

errors in the DEM is almost completely removed using a

small (e.g., 4 6 4 pixels) spatial window, but correlated noise

(e.g., forested or agricultural areas) requires a larger spatial

window (e.g., 32 6 32 pixels or larger).

Table 1. ALOS PALSAR images (path 325 frame 6390, FBS and

FBD, HH polarization) used in this study and perpendicular base-

line and time span calculated from first acquisition.

Image No. Date acquireda BH (m) t (days)

1 20070113 0 0

2 20070228 826 46

3 20070716 224 184

4 20070831 181 230

5 20071016 2397 276

6 20071201 2820 322

7 20080116 21066 368

8 20080302 21536 414

9 20080417 21816 460

10 20080902 3100 598

11 20081018 3217 644

12 20090118 2456 736

aDates are in year–month–day format.

Figure 2. Original (a) and corrected (b) linear deformation rates calculated from SBAS time series, and estimated residual topographic error

(c). Tauhara (bottom left) and Ohaaki (center) geothermal fields are outlined with black rectangles. Residual topographic noise is clearly

observed in (a).

Canadian Journal of Remote Sensing / Journal canadien de teledetection

E 2010 CASI S329

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

Results

We used our proposed modification of the SBAS tech-

nique to map subsidence at Tauhara and Ohaaki, the two

largest geothermal fields in the TVZ (Allis, 2000; Kissling

and Weir, 2005) that are extensively exploited for power

generation. Results of the C-band interferometric studies

were presented in Hole et al. (2007), and the most recent

results of ground leveling were presented in Allis et al.

(2009).

For this study 12 fine beam single (FBS) and fine beam

dual (FBD) polarization ALOS PALSAR images from path

325, frame 6390 (Table 1) were collected and processed from

raw format, and then FBD images were resampled to the

resolution of FBS images. Interferometric pairs with perpen-

dicular baseline less than 1000 m were selected, and standard

interferometric processing was performed that consisted of

coarse and then fine image coregistration to a single master,

interferogram formation, removal of earth curvature and

topographic phases using the 90 m Shuttle Radar Topography

Mission (SRTM) DEM, filtering, phase unwrapping, and

residual orbital correction using ground control points data.

The final resolution of produced differential interferograms is

approximately 30 m 6 30 m (multilooking 5 6 10), which is

significantly higher than the resolution of the DEM (90 m).

Higher resolution DEMs are available for New Zealand; how-

ever, their accuracy has not been validated, whereas the accu-

racy of the SRTM DEM is known to be about 7 m (Farr and

Kobrick, 2000).

Time series were calculated with the proposed modification

of the SBAS technique and linear deformation rates were esti-

mated for a 40 km 6 40 km subregion of the central TVZ. The

standard SBAS technique, without any topographic correction

(Lanari et al., 2004), and the modified SBAS technique pro-

posed in this paper were tested. Linear deformation rates cal-

culated with the standard SBAS are shown in Figure 2a and

with the modified SBAS in Figure 2b. The presence of topo-

graphic noise is clearly observed on the left image, such as

long linear features in the center of the image, and also spa-

tially correlated topographic noise caused by the growth of

vegetation in the bottom-right corner. The spatially uncorre-

lated noise is successfully removed in Figure 2b. It is also

possible to remove spatially correlated noise by choosing a

larger window (e.g. 64 6 64 pixels). The residual topographic

noise was calculated and presented in Figure 2c; its standard

deviation was estimated to be about 5 m. For a stable region

within this image we also estimated the standard deviation

for linear deformation rates calculated by fitting a linear

trend to the SBAS time series and for mean deformation rates

calculated by stacking before and after topographic correc-

tion was applied (Table 2). However, these calculations have

to be approached carefully because in a region such as the

TVZ, the selection of a stable region may not be accurate.

Table 2. Standard deviation estimated

for linear deformation rates calculated

by fitting linear trend to SBAS time series

and for mean deformation rates calcu-

lated by stacking before and after topo-

graphic correction applied.

SD (cm/year)

Linear rate 1.12

Corrected linear rate 0.66

Stack 0.90

Corrected stack 0.80

Figure 3. Time series analysis of Tauhara geothermal field deformation using modified SBAS technique applied to ascending images from

path 325. Subsidence is calculated between 20070113 (in year–month–day format), and date provided on images is caused by extraction of

groundwater. Each image is approximately 2.4 km 6 2.4 km.

Vol. 36, Suppl. 2, 2010

S330 E 2010 CASI

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

The 2D time series of subsidence observed by the pro-

posed technique at the Tauhara and Ohaaki geothermal

fields are presented in Figures 3 and 5. These images, with

an approximate spatial extent of 2.4 km 6 2.4 km, show

ground deformation occurring between 20070113 (master

image; date is in year–month–day format) and the date

shown. The maximum value of deformation is approxi-

mately 14 cm, which corresponds to about a 5–6 cm/year

rate of subsidence. For comparison purposes, time series

for the Tauhara geothermal fields were calculated by apply-

ing the proposed modification of the SBAS technique to a set

of descending ALOS PALSAR images from path 628, frame

4400 (Figure 4). The magnitude of ground deformation is

approximately similar for the ascending and descending set

of images, but their shape is different, which is due to the

presence of a large horizontal component. Unfortunately,

descending images from path 628 do not provide coverage

of the Ohaaki geothermal field.

Figure 4. Time series analysis of Tauhara geothermal field deformation using modified SBAS technique applied to descending images from

path 628. Subsidence is calculated between 20070715 (in year–month–day format), and date provided on images is caused by extraction of

groundwater. Each image is approximately 2.4 km 6 2.4 km.

Figure 5. Time series analysis of Ohaaki geothermal field deformation using modified SBAS technique applied to ascending images from

path 325. Subsidence is calculated between 20070113 (in year–month–day format), and date provided on images is caused by extraction of

groundwater. Each image is approximately 2.4 km 6 2.4 km.

Canadian Journal of Remote Sensing / Journal canadien de teledetection

E 2010 CASI S331

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

The 1D time series are also presented for the regions of

fastest subsidence at the Tauhara (ascending and descend-

ing) and Ohaaki geothermal fields (Figures 6a and 6b) and

for a randomly chosen region with a large residual topo-

graphic error (Figure 6c). The time series were calculated

using standard SBAS technique without applying any topo-

graphic correction (marked as uncorrected), and applying

standard regression (marked as regression) and proposed

topographic correction (marked as 32 6 32).

For the Tauhara geothermal field, both ascending and

descending set of images show approximately steady subsid-

ence with similar rate (slope). Application of a standard

topographic correction significantly distorts the time series.

In particular, for a descending set the corrected time series

are clearly distorted. The shape of distortion resembles the

temporal pattern of the perpendicular baseline observed in

Figure 1. At the same time, the time series calculated by

applying the proposed topographic correction are not dis-

torted. Another type of distortion is observed for the Ohaaki

geothermal field. Here standard regression produces time

series with underestimated rate, which is caused by the

correlation of the perpendicular baseline with the time of

acquisition.

Discussion and conclusions

The technique presented in this paper is a modification of

the Small Baseline Subset (SBAS) method applied to time

series analysis of ALOS PALSAR interferograms. It success-

fully estimates and removes residual topographic noise from

a set of intreferograms by performing a linear regression of

the residual phase versus perpendicular baseline. The resid-

ual phase is calculated for each coherent pixel as a difference

of the observed phase and its spatial average. The average

value is calculated in the spatial window and contains the

deformation signal contaminated with atmospheric noise.

Since the residual phase contains only the residual topo-

graphic component, the accuracy of the proposed topo-

graphic correction is higher than if it was calculated from

the observed phase alone.

Figure 6. Time series of ground deformation for Tauhara (a) and Ohaaki (b) geothermal fields and for region with large residual

topographic error (c).

Vol. 36, Suppl. 2, 2010

S332 E 2010 CASI

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

The proposed technique can be successfully applied

to a dataset from any satellite, but it is more relevant for

L band because the technique is coherent in densely vege-

tated environments even for interferometric pairs with a

large perpendicular baseline. In particular, this technique

is found to be useful for ALOS PALSAR time series analy-

sis because PALSAR images are acquired with large perpen-

dicular baselines whose lengths correlate with the time of

acquisition.

In this paper we used the technique for measuring sub-

sidence at the two largest geothermal fields, Tauhara and

Ohaaki, located in the central Taupo Volcanic Zone

(TVZ), New Zealand, using 12 ALOS PALSAR images

acquired between 13 January 2007 and 18 January 2009.

Steady subsidence with rates of 5–6 cm/year is clearly visible

on the presented time series for both geothermal fields, and

location and spatial extent of the subsidence is in agreement

with previous studies based on C-band interferometry and

ground leveling.

We also performed a comparison of the proposed tech-

nique with the standard SBAS technique. The residual topo-

graphic noise observed on the image calculated with the

original SBAS was successfully removed on the image calcu-

lated with our modified SBAS technique, simplifying its

interpretation. We have shown that L-band interferometry

can be successfully used for time series analysis, in spite of its

large wavelength and high sensitivity to residual topographic

noise, for mapping of ground deformation in densely vege-

tated regions where standard C-band sensors produced only

limited results. The nature of residual topographic noise is

not absolutely clear at this time and needs to be studied

further. We identified noise produced by vegetation. This

could be caused by either changes in land cover since the

time of acquisition of C-band SRTM data that was used

for DEM generation or different wave propagation and

interaction of C- and L-band SAR with vegetation and soil.

We also identified a few regions of a large residual topo-

graphic noise around Taupo as anthropogenic.

Acknowledgments

This work was supported by GNS Science and the

Foundation for Research, Science and Technology, New

Zealand. We thank John Beavan for reviewing the manu-

script. The ALOS PALSAR data was used in this work with

the permission of the Japan Aerospace Exploration Agency

(JAXA), the Ministry of Economy, Trade and Industry

of Japan (METI), and the Commonwealth of Australia

(Geoscience Australia) (‘‘the Commonwealth’’). JAXA,

METI, and the Commonwealth have not evaluated the data

as altered and incorporated within this work and therefore

give no warranty regarding its accuracy, completeness, cur-

rency, or suitability for any particular purpose. The work of

Sergey Samsonov was in part supported by the Luxembourg

National Research Fund (FNR). The images were plotted

with GMT software, and SRTM DEM data was provided by

the U.S. Geological Survey (USGS).

References

Allis, R. Review of subsidence at Wairakei field, New Zealand. 2000.

Geothermics, Vol. 29, No. 4–5, pp. 455–478.

Allis, R., Bromley, C.J., and Currie, S. 2009. 2009 Update on subsidence at

the Wairakei-Tauhara geothermal system, New Zealand. Geothermics,

Vol. 38, No. 1, pp. 169–180.

Berardino, P., Fornaro, G., and Lanari, R. 2002. A new algorithm for

surface deformation monitoring based on small baseline differential

SAR interferograms. IEEE Transactions on Geoscience and Remote Sens-

ing, Vol. 40, No. 11, pp. 2375–2383.

Farr, T., and Kobrick, M. Shuttle Radar Topography Mission produces a

wealth of data. 2000. EOS Transactions, AGU, Vol. 81, pp. 583–585.

Ferretti, A., Prati, C., and Rocca, F. 2000. Nonlinear subsidence rate

estimation using permanent scatterers in differential SAR interferometry.

IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 5,

pp. 2202–2212.

Ferretti, A., Prati, C., and Rocca, F. 2001. Permanent scatterers in SAR

interferometry. IEEE Transactions on Geoscience and Remote Sensing,

Vol. 39, pp. 8–20.

Ferretti, A., Novali, F., Burgmann, R., Hilley, G., and Prati, C. 2004.

InSAR permanent scatterer analysis reveals ups and downs in San Fran-

cisco Bay area. EOS Transactions, AGU, Vol. 85, p. 34.

Hole, J., Bromley, C., Stevens, N., and Wadge, G. 2007. Subsidence in the

geothermal fields of the Taupo Volcanic Zone, New Zealand from 1996

to 2005 measured by InSAR. Journal of Volcanology and Geothermal

Research, Vol. 166, No. 3–4, pp. 125–146.

Hooper, A. 2008. A multi-temporal InSAR method incorporating both

persistent scatterer and small baseline approaches. Geophysical Research

Letters, Vol. 35, L16302.

Jacobs, A., Sandwell, D., Fialko, Y., and Sichoix, L. 2002. The 1997 (M 7.1)

Hector Mine, California, earthquake: near-field postseismic deformation

from ERS interferometry. BSSA Special Issue on Hector Mine Earth-

quake, Vol. 92, No. 4, pp. 1433–1442.

Kissling, W.M., and Weir, G.J. 2005. The spatial distribution of the geo-

thermal fields in the Taupo Volcanic Zone, New Zealand. Journal of

Volcanology and Geothermal Research, Vol. 145, No. 1–2, pp. 136–150.

Kwoun, O., Lu, Z., Neal, C., and Wicks, C. 2006. Quiescent deformation of

the Aniakchak Caldera, Alaska, mapped by InSAR. Geology, Vol. 34,

No. 1, pp. 5–8.

Lanari, R., Mora, O., Manunta, M., Mallorqui, J.J., Berardino, P., and

Sansosti, E. 2004. A small-baseline approach for investigating deforma-

tions on full-resolution differential SAR interferograms. IEEE Transac-

tions on Geoscience and Remote Sensing, Vol. 42, No. 7, pp. 1377–1386.

Lu, Z., Mann, D., Freymueller, J., and Meyer, D. 2000. Synthetic aperture

radar interferometry of Okmok volcano, Alaska: Radar observations.

Journal of Geophysical Research, Vol. 105, No. B5, pp. 10791 – 10 806.

Lu, Z., Wicks, C., Dzurisin, D., Power, A., Moran, S., and Thatcher, W.

2002. Magmatic inflation at a dormant stratovolcano: 1996–1998 activity

at Mount Peulik volcano, Alaska, revealed by satellite radar interfero-

metry. Journal of Geophysical Research, Vol. 107, No.B7.

Canadian Journal of Remote Sensing / Journal canadien de teledetection

E 2010 CASI S333

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.

Lyons, S., and Sandwell, D. 2003. Fault creep along the southern San

Andreas from interferometric synthetic aperture radar, permanent scat-

terers and stacking. Journal of Geophysical Research, Vol. 108, No. B1,

p. 2047.

Massonnet, D., and Feigl, K. 1998. Radar interferometry and its application

to changes in the Earth surface. Reviews of Geophysics, Vol. 36, No. 4,

pp. 441–500.

Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., and Feigl, K.

1993. The displacement field of the Landers earthquake mapped by radar

interferometry. Nature, Vol. 364, pp. 138–142.

Massonnet, D., Briole, P., and Arnaud, A. 1995. Deflation of Mount Etna

monitored by spaceborne radar interferometry. Nature, Vol. 375, pp. 638–

643.

Rosen, P., Hensley, P., Joughin, I., Li, F., Madsen, S., Rodriguez, E., and

Goldstein, R. 2000. Synthetic aperture radar interferometry. Proceedings

of the IEEE, Vol. 88, No. 3, pp. 333–382.

Rosenqvist, A., Shimada, M., Ito, N., and Watanabe, M. 2007. ALOS

PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the

Environment. IEEE Transactions on Geoscience and Remote Sensing,

Vol. 45, No. 11, pp. 3307–3316.

Sandwell, D., and Price, E. 1998. Phase gradient approach to stacking inter-

ferograms. Journal of Geophysical Research, Vol. 103, No. B12,

pp. 30183 – 30 204.

Shmidt, D., and Burgmann, R. 2003. Time-dependent land uplift and sub-

sidence in the Santa Clara valley, California, from a large interferometric

synthetic aperture radar data set. Journal of Geophysical Research,

Vol. 108, No. B9, p. 2416.

Usai, S. A least squares database approach for SAR interferometric data.

2003. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41,

No. 4, pp. 753–760.

Wright, T., Parsons, B., England, P., and Fielding, E. 2004. InSAR observa-

tions of low slip rates on the major faults of western Tibet. Science,

Vol. 305, pp. 236–239.

Wyss, M. 2001. Locked and creeping patches along the Hayward fault,

California. Geophysical Research Letters, Vol. 28, No. 18, pp. 3537–

3540.

Vol. 36, Suppl. 2, 2010

S334 E 2010 CASI

Can

adia

n Jo

urna

l of

Rem

ote

Sens

ing

Dow

nloa

ded

from

pub

s.ca

si.c

a by

Nat

ural

Res

ourc

es C

anad

a on

09/

27/1

1Fo

r pe

rson

al u

se o

nly.