Volcanology: Lessons learned from Synthetic Aperture Radar imagery
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Volcanology: Lessons learned from Synthetic Aperture Radar imagery
V. Pinel, M.P. Poland, A. Hooper
PII: S0377-0273(14)00308-4DOI: doi: 10.1016/j.jvolgeores.2014.10.010Reference: VOLGEO 5430
To appear in: Journal of Volcanology and Geothermal Research
Received date: 26 June 2014Accepted date: 11 October 2014
Please cite this article as: Pinel, V., Poland, M.P., Hooper, A., Volcanology: Lessonslearned from Synthetic Aperture Radar imagery, Journal of Volcanology and GeothermalResearch (2014), doi: 10.1016/j.jvolgeores.2014.10.010
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Volcanology: Lessons learned from Synthetic Aperture
Radar imagery
V. Pinela, M. P. Polandb, A. Hooperc
aISTerre, Universite de Savoie, IRD, CNRS, F73376 Le Bourget du Lac, FrancebU.S. Geological Survey Hawai‘ian Volcano Observatory, PO Box 51, Hawai‘i National
Park, HI 97818-0051, USAcCOMET, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT
Abstract
Twenty years of continuous Earth observation by satellite SAR has re-
sulted in numerous new insights into active volcanism, including a better
understanding of subsurface magma storage and transport, deposition of vol-
canic materials on the surface, and the structure and development of volcanic
edifices. This massive archive of data has resulted in fundamental leaps in
our understanding of how volcanoes work–for example, identifying magma
accumulation at supposedly quiescent volcanoes, even in remote areas or in
the absence of ground-based data. In addition, global compilations of vol-
canic activity facilitate comparison of deformation behavior between different
volcanic arcs and statistical evaluation of the strong link between deforma-
tion and eruption. SAR data are also increasingly used in timely hazards
evaluation thanks to decreases in data latency and growth in processing and
analysis techniques. The existing archive of SAR imagery is on the cusp of
being enhanced by a new generation of satellite SAR missions, in addition
Email address: [email protected] (V. Pinel)
Preprint submitted to Journal of Volcanology and Geothermal Research October 22, 2014
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to ground-based and airborne SAR systems, which will provide enhanced
temporal and spatial resolution, broader geographic coverage, and improved
availability of data to the scientific community. Now is therefore an oppor-
tune time to review the contributions of SAR imagery to volcano science,
monitoring, and hazard mitigation, and to explore the future potential for
SAR in volcanology. Provided that the ever-growing volume of SAR data
can be managed effectively, we expect the future application of SAR data to
expand from being a research tool for analyzing volcanic activity after the
fact, to being a monitoring and research tool capable of imaging a wide va-
riety of processes on different temporal and spatial scales as those processes
are occurring. These data can then be used to develop new models of how
volcanoes work and to improve quantitative forecasts of volcanic activity as
a means of mitigating risk from future eruptions.
Keywords:
SAR, volcanoes, deformation, eruptive deposits, DEM
Contents
1 Introduction 4
2 Synthetic Aperture Radar analysis techniques and available
data 8
2.1 Synthetic Aperture Radar principles . . . . . . . . . . . . . . . 8
2.2 Surface change detection . . . . . . . . . . . . . . . . . . . . . 10
2.3 Retrieval of topography through InSAR . . . . . . . . . . . . . 11
2.4 Retrieval of displacement . . . . . . . . . . . . . . . . . . . . . 13
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2.4.1 Displacement from InSAR . . . . . . . . . . . . . . . . 14
2.4.2 Pixel offset tracking . . . . . . . . . . . . . . . . . . . . 15
2.4.3 Multiple-aperture interferometry (MAI) . . . . . . . . 16
2.4.4 Precise positioning . . . . . . . . . . . . . . . . . . . . 17
2.5 Time series processing . . . . . . . . . . . . . . . . . . . . . . 18
2.5.1 Persistent scatterer InSAR . . . . . . . . . . . . . . . . 19
2.5.2 Small baseline InSAR . . . . . . . . . . . . . . . . . . . 20
2.5.3 Combined time series InSAR . . . . . . . . . . . . . . . 21
2.6 SAR platforms and available data . . . . . . . . . . . . . . . . 22
3 Mapping surface characteristics with SAR 26
3.1 Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Phase used for topographic measurement . . . . . . . . . . . . 31
4 Insights from SAR into volcano deformation 35
4.1 Sources of deformation around volcanoes . . . . . . . . . . . . 36
4.2 Overview of volcano deformation studies based on SAR data . 41
4.2.1 Magma storage . . . . . . . . . . . . . . . . . . . . . . 42
4.2.2 Magma transport . . . . . . . . . . . . . . . . . . . . . 46
4.2.3 Temporal evolution of magmatic deformation . . . . . 49
4.2.4 Subsidence of volcanic deposits . . . . . . . . . . . . . 52
4.3 Main InSAR limitations for deformation measurements . . . . 55
5 Key constraints on volcanic edifice growth and stability 58
6 Discussion 63
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6.1 Looking back: advances made possible from SAR studies of
volcanoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2 Looking forward: better understanding of volcanoes and fore-
casts of eruptions . . . . . . . . . . . . . . . . . . . . . . . . . 67
7 Conclusion 70
8 Acknowledgments 71
1. Introduction1
The advent of satellite remote sensing brought about a revolution in the2
field of volcanology. Before the availability of space-based observations, vol-3
cano monitoring and research relied on painstaking field work to measure4
such parameters as ground deformation, gas emissions, and deposit charac-5
teristics. While valuable and, in many cases, groundbreaking, such as the6
efforts that led to successful predictions of dome-building eruptions at Mount7
St. Helens in the 1980s (Swanson et al., 1983), this work was necessarily lim-8
ited in scope (both in terms of spatial coverage and temporal sampling), and9
relatively few volcanoes were intensely studied. Satellite data have made10
possible a new spectrum of measurements on a global scale that can com-11
plement more focused ground-based studies and can also reveal insights into12
remote or poorly understood volcanoes (Sparks et al., 2012; Pyle et al., 2013).13
Passive measurements in the visible, ultra-violet, and infrared parts of the14
spectrum have been used to detect eruptions, quantify the compositions and15
distributions of deposits, characterize structures, and monitor thermal, ash,16
and gas emissions in near-real time–a critical capability for such applications17
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as eruption alerts (Wright et al., 2004a) and aviation safety (Hooper et al.,18
2012b). Active measurements from Synthetic Aperture Radar (SAR) have19
further transformed the field of volcano remote sensing, especially since the20
early 1990s. While useful for mapping structures and deposits, SAR offers21
the additional capabilities of quantifying topography and tracking surface de-22
formation, and it is not impacted by time of day or atmospheric conditions.23
Deformation studies, in particular, have exploded thanks to interferomet-24
ric SAR (InSAR). Biggs et al. (2014) reported that 198 volcanoes had been25
systematically observed by InSAR since the launch of the ERS-1 satellite,26
which is more than four times the number of volcanoes where deformation27
studies had been performed by the late 1990s (Dvorak and Dzurisin, 1997).28
Among these 198 volcanoes studied by InSAR over the course of 1992–2010,29
54 volcanoes displayed some indication of deformation, and 25 of those vol-30
canoes erupted during the same interval (Biggs et al., 2014). Including volca-31
noes that have not been systematically monitored on decadal timescales, In-32
SAR results have been reported from 620 volcanoes (out of a total of over 150033
subaerial Holocene volcanoes worldwide see http://www.volcano.si.edu/list volcano.cfm34
), with 161 observed to deform (Biggs et al., 2014). Dzurisin (2003) pointed35
out that aseismic inflation, which is readily detected by InSAR, might be an36
indicator of potential volcanic activity on intermediate timescales of years37
to months – a problematic forecasting window in volcanology – since such38
deformation would indicate magma accumulation that had not yet stressed39
the surrounding rocks to the point of breaking. His foresight has largely been40
borne out by InSAR surveys of entire volcanic arcs (e.g., Pritchard and Si-41
mons, 2002, 2004; Chaussard and Amelung, 2012; Ebmeier et al., 2013a; Lu42
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and Dzurisin, 2014) that have detected deformation at a number of sup-43
posedly quiescent volcanoes, some of which subsequently erupted. Such44
applications demonstrate the power of InSAR – comprehensive, repeated45
deformation monitoring over broad regions that requires no ground-based46
instrumentation or field personnel.47
The utility of SAR extends beyond deformation monitoring. Variations48
in InSAR coherence over time, which provide a measure of the correlation49
in the scattering properties of the surface, can be used to map, for example,50
lava flow evolution (e.g., Dietterich et al., 2012). InSAR can also quantify51
topography – a critical base for general mapping, and an essential input to52
models of hazardous processes like lava flows (Harris and Rowland, 2001;53
Favalli et al., 2011), pyroclastic deposits (Kelfoun et al., 2009), lahars (Iver-54
son et al., 1998) and debris avalanches (Kelfoun et al., 2008). SAR amplitude55
data have proven their value by detecting structural changes at volcanoes,56
as emphatically demonstrated during the 2010 eruption of Merapi, Indonesia57
(Pallister et al., 2013; Surono et al., 2012). The ability of SAR imagery to see58
through dense cloud cover and the delivery of those data in near-real time59
facilitated hazards mitigation efforts that probably prevented extensive loss60
of life during that eruption (Pallister et al., 2013).61
Since the 1991 launch of the European Space Agency’s (ESA) ERS-1,62
there has always been at least one SAR satellite, and frequently more, in orbit63
around Earth. The first application of these data to assess volcano deforma-64
tion was monitoring subsidence of Mount Etna during 1992-1993 (Massonnet65
et al., 1995). In the 20 years since that landmark result, SAR satellites have66
evolved from mostly C-band sensors with ground-pixel resolutions of tens of67
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meters to C-, X- and L-band systems that can have resolutions better than68
1 m, with some satellites flying in formation or as part of a constellation.69
The scientific community stands at the cusp of a “golden age” for SAR, with70
existing space-based missions about to be joined by a new generation of satel-71
lites in the forthcoming years, including the ESA’s Sentinel-1–the first SAR72
mission that is operational in nature rather than purely scientific, with data73
available within minutes of acquisition. These new satellites will provide74
data that volcanologists have identified as critical to detecting, tracking and75
understanding eruptive activity, as highlighted by a number of international76
programs. For example, the Geohazard Supersites and Natural Laboratories77
(GSNL) initiative ( http://supersites.earthobservations.org/) is designed to78
focus attention on areas prone to natural hazards by integrating ground-,79
air-, and space-based observations and making these data openly available80
to all researchers at no cost. SAR data are a particular focus of the GSNL81
program, and several volcanic regions have been identified as permanent Su-82
persites (including, as of 2014, Hawai‘i, Iceland, and Italy), with several83
candidate and event Supersites being established as well. Similarly, the 201284
International Forum on Satellite Earth Observations for Geohazards (also85
known as the Santorini conference) specifically advocated that SAR have an86
expanded role in volcano monitoring and research (Bally, 2012). Airborne87
and ground-based SAR are also seeing greater use at volcanoes around the88
world, adding a new dimension to volcanological investigations (e.g., Lund-89
gren et al., 2013; Intrieri et al., 2013).90
The imminent availability of satellite imagery from new SAR systems,91
greater use of ground and airborne SAR, and recent community efforts to92
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ensure greater access to SAR data provide an opportune occasion to review93
20 years of progress in the volcanological applications of SAR and to an-94
ticipate future advances. Previous reviews, for example by Massonnet and95
Sigmundsson (2000) and Zebker et al. (2000) or also by Stevens and Wadge96
(2004) and d’Oreye et al. (2008), supply an important foundation upon which97
to build, and a reference to which we can relate results achieved since the turn98
of the current century. We begin our review by discussing those principles of99
SAR that make the technique valuable for volcano monitoring and research.100
We then describe application of SAR to volcanoes, including mapping of101
structures and deposits, quantifying topography, and especially tracking sur-102
face deformation, before concluding with an examination of how integrating103
results from SAR can elucidate large-scale dynamic processes and offering104
our perspective on the future of SAR in volcanology.105
2. Synthetic Aperture Radar analysis techniques and available data106
2.1. Synthetic Aperture Radar principles107
The SAR technique allows the formation of high-resolution radar images108
from data acquired by side-looking instruments installed on aircraft or space-109
craft, or even from the ground. The fundamentals underlying SAR image110
processing are presented in e.g., Curlander and McDonough (1992). Each111
pixel of an image corresponds to a resolution element on the ground, which112
receives and scatters back an electromagnetic signal emitted by the radar. A113
pixel is characterized by two values: the amplitude and the phase. The ampli-114
tude can be interpreted in terms of backscattering properties of the ground.115
The phase is not informative on its own because it is a pseudo-random contri-116
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bution from the configuration of all scatterers within the resolution element.117
However, providing the scattering properties of the element remain stable118
between two acquisitions, the difference in phase between two images can be119
interpreted in terms of the difference in range between the radar instrument120
and the target, which is the principle of radar interferometry (see Section 2.3).121
122
SAR imaging geometry is characterized by two directions: the “azimuth”123
direction being the direction of satellite motion and the “range” direction124
corresponding to the look direction of the radar, which is approximately125
perpendicular to azimuth. Resolution elements in the range direction are126
distinguished by their distance from the satellite, which differs from the case127
of optical images where resolution elements are differentiated by viewing an-128
gle. The combination of the side-looking nature of the sensor and topography129
on the ground mean that the ground surface is often not completely imaged130
by the SAR. Surfaces oriented on ground sloping away from the sensor can131
be in a shadow zone not reached by the radar beam–an effect called ”shadow-132
ing”. When the ground slope is greater than the incidence angle (measured133
from vertical) of the radar signal, upslope becomes closer to the sensor than134
downslope, and the order of pixels in the image becomes reversed–the so-135
called “layover” effect (Figure 1).136
Two images acquired at the same time can be used to image the static137
topography, but to detect and quantify surface changes with time, at least138
two images acquired at different times need to be compared. Before proceed-139
ing with the processing, there is a need to put both images into the same140
geometry, as the sensor never acquires successive images from exactly the141
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same position. The geometry of one image is chosen as the “master” geom-142
etry and the second “slave” image is resampled into the “master” geometry,143
such that corresponding pixels in both images correspond to the same area144
of ground. This co-registration is achieved based on orbit knowledge and re-145
fined using amplitude image correlation (see section 5.2.1 of Dzurisin (2007)146
for a detailed description of the processing).147
2.2. Surface change detection148
The radar echo for a given pixel depends on the coherent sum of the149
echo from all scatterers within the corresponding resolution element; thus,150
the amplitude and phase of the echo are sensitive to any change in the dis-151
tribution of scatterers within the element. It follows that if scatterers move152
with respect to each other or, as in case of new emplacement of surface lava153
flows, are replaced by a new set of scatterers, this can be detected in a series154
of SAR images. Detection methods are based either on the evolution of the155
reflectivity, that is to say, the amplitude of the radar images–or on changes156
in the correlation of the signal, which is a measure of the phase change.157
Variations in radar amplitude are most often quantified by differencing the158
amplitude between two successive acquisitions (e.g., Wadge et al., 2011) or by159
calculating the ratio between amplitudes (e.g., Wadge et al., 2002a). Decor-160
relation of the signal is estimated by calculation of the “coherence” between161
two acquisitions, which is a complex entity defined as (Zebker et al., 1996):162
ρ =E [z1z
∗
2]
√
E [|z1|2] E [|z2|2], (1)
where z1 and z2 are the signal values from the two images, represented as163
complex numbers, and E[x] refers to the expected value of x– in other words,164
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the mean value of an infinite number of realisations of x. The expected values165
are usually estimated by spatial averaging over a finite region. A reduction in166
the magnitude of the coherence, which ranges between zero and one, indicates167
decorrelation. Various sources of decorrelation include: thermal decorrela-168
tion, which is due to the influence of thermal noise on the sensor and can be169
estimated theoretically by deriving the signal-to-noise ratio of a specific sys-170
tem; geometric and Doppler centroid decorrelation, which are, respectively,171
due to differences in the incidence angle and in Doppler centroid frequencies172
between two acquisitions; temporal decorrelation, which is caused by any173
change in the distribution of wavelength-scale scatterers within a resolution174
cell, or of their electrical characteristics; and volume decorrelation, which175
is related to the penetration of the radar waves and is dependent on the176
radar wavelength and the scattering medium. For a more complete review of177
decorrelation causes, see Lu and Dzurisin (2014) and references cited therein.178
179
2.3. Retrieval of topography through InSAR180
As mentioned above, while the phase of an individual SAR image cannot181
be easily interpreted, the phase difference between two coregistered images182
relates to the difference in range between the two images. This difference in183
range can in turn be related to the elevation of the ground. SAR interfer-184
ometry (InSAR) involves computing the product of a master image and the185
complex conjugate of a coregistered slave image. The phase of the resultant186
“interferogram” is equal to the difference in phase between the master and187
slave images (the InSAR technique is described in detail by Massonnet and188
Feigl, 1998; Burgmann et al., 2000; Dzurisin, 2007; Massonnet and Souyris,189
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2008).190
There is a contribution to the interferometric phase from the differing191
viewing geometries of master and slave images, which can be divided into192
the phase expected if the surface of the Earth followed a reference ellipsoid193
(the so-called “flat Earth phase”, φFE) and the phase due to deviations of194
the real Earth surface from the reference surface due to topography (φtopo).195
In addition, there is a contribution from the displacement of the pixel in196
the satellite line-of-sight (LOS) direction (φdef ) and a contribution from the197
difference in the phase delay during propagation of the signal through the198
atmosphere between acquisitions (φatm). Thus, the interferometric phase for199
each pixel can be described as200
φ = W{φFE + φdef + φtopo + φatm + φN}, (2)
where φN is a phase noise term and W{·} is an operator that drops whole201
phase cycles (known as “wrapping”), as only the fractional part of the phase202
can actually be measured. The phase noise term includes thermal noise, but203
is usually dominated by decorrelation due both to the relative movement204
of scatterers (mentioned above) which typically increases with time, and205
differences in viewing angle between the two acquisitions, which also cause206
the scatterer echoes to sum differently. The difference in viewing angle is207
usually expressed as the “perpendicular baseline”, (B⊥) between the two208
acquisitions, which is the component of the baseline perpendicular to the209
line of sight.210
Given an accurate description of the satellite orbits, the flat Earth phase211
can be easily calculated. In the case where φdef and φatm can be considered212
negligible, e.g., for two images acquired at the same time, the interferometric213
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phase difference can then be interpreted as being due to topography only to214
produce a digital elevation model (DEM):215
φtopo ≈ W
{
−4πB⊥h
λR sin θ
}
, (3)
where R is the distance between the surface and satellite, h is the elevation216
of the surface above the reference surface, λ is the radar wavelength and θ is217
the angle of incidence. Topography in InSAR phase can also be expressed in218
terms of the “altitude of ambiguity”, ha, which is defined as the change in219
elevation that results in one complete phase cycle, i.e., a topographic fringe,220
Equation 3 becoming221
φtopo ≈ W
{
−2πh
ha
}
. (4)
The accuracy of topographic measurement therefore improves with increased222
perpendicular baseline, which corresponds to a smaller altitude of ambiguity,223
although if the perpendicular baseline is too large the interferogram will not224
be coherent.225
InSAR does not provide absolute heights, as the phase only records the226
the fractional part of each phase cycle; however, the relative elevation be-227
tween two pixels in an interferogram can be estimated by integrating the228
phase gradient between them, a process known as “phase unwrapping” (Chen229
and Zebker, 2001).230
231
2.4. Retrieval of displacement232
Displacements can be retrieved from SAR data using a variety of methods,233
the most accurate of which is the InSAR technique, although this only gives234
displacement in the line-of-sight direction. Two other techniques can be used235
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to retrieve displacement in the azimuth direction, but they are less accurate236
than InSAR (see below). When the line-of-sight deformation is very large,237
the InSAR technique may fail and the less accurate techniques are then also238
useful in the range direction. While the aforementioned techniques provide239
measurements of relative displacement between image pixels, with modern240
high-resolution sensors it is also possible to obtain absolute measurements of241
displacement for artificial scatterers using ranging.242
Combining the displacement information in azimuth and range directions243
for both ascending and descending acquisitions, it is then possible to invert244
for the 3D displacement field (Wright et al., 2004b). For a complete review245
on retrieval of the 3D displacement field using only SAR measurements or246
integrating SAR data with GPS, see Hu et al. (2014).247
2.4.1. Displacement from InSAR248
If an interferogram is created from two images acquired at different times,249
the component of displacement in the line of sight, l, can be retrieved from250
the interferometric phase,251
φdef = W
{
−4πl
λ
}
(5)
As the interferometric phase also contains other terms (Equation 2), these252
must be reduced as much as possible in order to retrieve the displacement.253
Orbit data can be used to calculate φFE and a DEM can be used to estimate254
φtopo. φN is usually reduced, at the cost of resolution, by summing many255
neighboring pixels in space (“multilooking”); however, the atmospheric term256
φatm can be significant and difficult to reduce. Although in principle the257
accuracy of displacement should be much smaller than the wavelength (λ),258
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in practice it is limited by the atmospheric term (see Section 4.3).259
Similar to topography estimation (Section 2.3), InSAR does not provide260
an absolute value for l, as the phase only records the fractional part of each261
phase cycle; however, the relative line-of-sight displacement between any two262
pixels in an interferogram can be estimated by unwrapping the phase between263
them.264
In summary, InSAR allows to us quantify the projection of the ground265
displacement along the line-of-sight direction with an accuracy on the order266
of a cm or two. To quantify small displacement rates over a long period of267
time, specific algorithms for times series of SAR data processing have been268
developed (see Section 2.5).269
2.4.2. Pixel offset tracking270
Displacements in volcanic areas can be estimated from optical imagery271
by calculating pixel offsets, and the same principle can be applied to SAR272
imagery. Orbit information supplemented by amplitude image correlation273
is used to put all images in a common geometry by performing a global274
geometrical transformation. Pixels affected by large displacements between275
two acquisitions are characterized by residual offsets between the coregis-276
tered images. Their determination by finer amplitude correlation provides277
measurements of surface displacements in both azimuth and range directions.278
The accuracy of the technique depends on coherence (De Zan, 2014) but279
is on the order of one tenth of the spatial resolution, i.e., a few decimeters280
to a few meters. The troposphere has a limited effect on the accuracy pixel281
offsets, but the influence of ionospheric disturbances can be strong (e.g., Gray282
et al., 2000; Oyen et al., in prep). This method has been applied to quantify283
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large displacement fields in volcanic areas (e.g., Wright et al., 2006; Grandin284
et al., 2009).285
2.4.3. Multiple-aperture interferometry (MAI)286
An alternative method for estimating pixel offsets between images relies287
on splitting the frequency spectrum into two or more sub-apertures. This288
idea was first developed by Scheiber and Moreira (2000) to estimate misreg-289
istration between images in range and azimuth and is referred to as “spectral290
diversity”. The specific application of this technique to measure displacement291
in the azimuth direction was termed “multiple-aperture interferometry” by292
Bechor and Zebker (2006), and its application has been further improved293
by e.g., Jung et al. (2009). The method consists of using sub-aperture pro-294
cessing techniques to form one forward-looking and one backward-looking295
image from one SAR image. Each of these images is then combined with the296
corresponding forward and backward looking images obtained from a second297
SAR acquisition to form one forward-looking and one backward looking inter-298
ferogram. The phase difference between the forward-looking and backward-299
looking interferograms is called an MAI image and contains the displacement300
between the two acquisition times in the azimuthal (along-track) direction.301
MAI measurements are independent of the radar wavelength but require co-302
herence between the images. Like pixel offset tracking, the accuracy of the303
MAI technique depends on coherence, but it is better than offset tracking by304
a factor of 3 for a coherence of 0.5 (De Zan, 2014), and by a higher factor for305
lower coherence. However, these theoretical factors apply only when the am-306
plitude variation is due to speckle. In the case of amplitude contrast caused307
by variations in actual radar reflectivity, the performance of offset tracking308
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improves relative to MAI.309
Any tropospheric variations with wavelengths larger than 5 meters should310
have the same effect on both the forward and backward-looking interfer-311
ograms such that the displacement measurement is, in principle, not sig-312
nificantly limited by tropospheric contributions (Bechor and Zebker, 2006).313
However, in their study of the 2010 Merapi, Indonesia, eruption, De Michele314
et al. (2013) have shown that the presence of a volcanic ash plume may af-315
fect the backward and forward-looking images differently. As is the case for316
pixel offset tracking, the influence of ionospheric disturbances can also have317
a strong effect on MAI images (e.g., Jung et al., 2013; Oyen et al., in prep).318
2.4.4. Precise positioning319
The techniques for measuring displacement described above provide only320
the relative displacement between any two pixels within the acquisition area.321
With more recent high-resolution satellites, which also have better orbital322
tracking than earlier satellites, it is now possible to position individual point323
scatterers in a global reference frame with an accuracy of a few cm (Eineder324
et al., 2011; Schuber et al., 2012). This is achieved through ranging, and325
opens the door to measurement of displacement using individual artificial326
reflectors on the ground, by repeated point positioning. As with relative327
displacement techniques, a single acquisition geometry can only provide dis-328
placements in 2-D (azimuth and range), but a combination of ascending and329
descending images allows for 3D displacement measurement.330
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2.5. Time series processing331
Displacements can be estimated more accurately by processing many im-332
ages together, rather than the two-image approaches described above. Most333
multi-image algorithms have concentrated on retrieving displacement from334
interferometric phase (Section 2.4.1), although in one approach, pixel offsets335
(Section 2.4.2) have also been incorporated (Casu et al., 2011). For a review336
on SAR interferometry time series analysis, see Hooper et al. (2012a).337
The simplest approach for combining many images is to sum or “stack”338
the unwrapped phase of many conventionally formed interferograms (e.g.,339
Zebker et al., 1997). Persistent deformation is highlighted in interferometric340
stacks, whereas other random signals, like atmospheric anomalies, are sup-341
pressed. This approach, however, is only appropriate when the deformation342
is episodic (with no change in source parameters over time) or steady-state,343
with no seasonal deformation. Another limitation comes from the fact that344
the non-deformation signals are reduced only by averaging and cannot be345
explicitly estimated. Algorithms for time series analysis of SAR data have346
therefore been developed to better address these issues facing conventional347
InSAR; decorrelation is addressed by using phase behavior over time to se-348
lect pixels for which decorrelation noise is minimized, and non-deformation349
signals are estimated by a combination of modeling and filtering of the time350
series. These time series algorithms fall into two categories, the first being351
persistent scatterer InSAR, which targets pixels whose scattering properties352
remain consistent both in time and from variable look directions, and the353
second being the more general small baseline approach.354
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2.5.1. Persistent scatterer InSAR355
Decorrelation is caused by the contributions from scatterers within a res-356
olution element summing differently between SAR acquisitions. This can be357
due to relative movement of the scatterers, a change in the looking direction358
of the radar platform, or the appearance or disappearance of scatterers, as359
in the case of snow cover. If one scatterer returns significantly more energy360
than other scatterers within a resolution element, however, decorrelation is361
reduced. This is the principle behind a “persistent scatterer” (PS) pixel362
(sometimes referred to as a “permanent scatterer”). In urban environments,363
the dominant scatterers can be roofs oriented such that they reflect energy364
directly back to the radar, like a mirror, or the result of a “double-bounce”,365
where energy is reflected once from the ground, and once from a perpendic-366
ular structure, returning directly to the radar (Perissin and Ferretti, 2007).367
Dominant scatterers can also occur in areas without manmade structures368
(e.g., appropriately oriented rocks or blocks in lava fields), but there are369
fewer of them, and they tend to be less dominant.370
PS algorithms operate on a time series of interferograms all formed with371
respect to a single “master” SAR image. Phase unwrapping is achieved either372
using a temporal evolution model or algorithms that only assume that the373
temporal evolution should be generally smooth (Hooper, 2010). In both ap-374
proaches, deformation phase is separated from atmospheric phase and noise375
by filtering in time and space, the assumption being that deformation is cor-376
related in time, atmosphere is correlated in space but not in time, and noise377
is uncorrelated in space and time. In comparative studies between the two378
approaches, estimates for the deformation agree quite well, but the second379
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approach tends to result in better coverage, particularly in rural areas where380
most volcanoes are located (Doin et al., 2010; Sousa et al., 2011).381
The result of PS processing is a time series of displacement for each PS382
pixel, with much reduced noise terms (figure 2). The technique also has the383
advantage of being able to associate the deformation with a specific scatterer,384
rather than a resolution element that has dimensions dictated by the radar385
system– usually on the order of many meters. For volcano deformation stud-386
ies this level of detail is generally not required, although it can be useful in387
separating broader deformation from the local displacements associated with388
specific structures (e.g., faults, small hydrothermal features, and localized389
subsidence features).390
2.5.2. Small baseline InSAR391
A drawback of the PS technique for volcanic applications is that the392
number of PS pixels in a volcanic environment may be limited. However,393
by forming interferograms only between images separated by a short time394
interval and with a small difference in look direction (i.e., a small baseline),395
decorrelation is minimized and for some resolution elements can be small396
enough that the underlying deformation signal is still detectable. Pixels for397
which the phase decorrelates little over short time intervals are the targets398
of small baseline methods.399
Interferograms are formed between SAR images with a small difference400
in time and look angle. In many small baseline algorithms, the interfer-401
ograms are multilooked to further decrease decorrelation noise (Berardino402
et al., 2002; Fornaro et al., 2009), however, there may be isolated ground res-403
olution elements with low decorrelation that are surrounded by elements with404
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high decorrelation, such as a small clearing in a forest, for which multilooking405
will increase the noise. Other algorithms have therefore been developed that406
operate at full resolution (Lanari et al., 2004; Hooper, 2008), with the option407
to reduce resolution later in the processing chain by “smart” multilooking.408
Pixels are selected based on their estimated spatial coherence in each of the409
interferograms, using either standard coherence estimation, or enhanced tech-410
niques in the case of full-resolution algorithms. The phase is then unwrapped411
either spatially in two dimensions (e.g., Chen and Zebker, 2001), or using the412
additional dimension of time in 3-D approaches (e.g., Pepe and Lanari, 2006;413
Hooper, 2010). At this point, the phase can be inverted to give the phase414
at each acquisition time with respect to a single image, using least-squares415
(Schmidt and Burgmann, 2003), singular value decomposition (Berardino416
et al., 2002), or minimization of the L1-norm (Lauknes et al., 2011). Separa-417
tion of deformation and atmospheric signals can be achieved by filtering the418
resulting time series in time and space, as in the PS approach. Alternatively,419
if an appropriate model for the evolution of deformation in time is known,420
the different components can be directly estimated from the small baseline421
interferograms (Biggs et al., 2007).422
2.5.3. Combined time series InSAR423
Because persistent scatterer and small baseline approaches are optimized424
for resolution elements with different scattering characteristics, they are com-425
plimentary, and techniques that combine both approaches are able to extract426
the signal with greater coverage than either method alone (Hooper, 2008; Fer-427
retti et al., 2011). Depending on the data set, some pixels can be selected428
by both approaches, but some pixels are only selected by one method or the429
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other (Figure 3).430
2.6. SAR platforms and available data431
Application of SAR to volcanoes began with the first deployments of432
orbital radar instruments in the 1970s and accelerated rapidly starting in433
the 1990s (Table 1). SEASAT was the first satellite SAR to orbit Earth (L-434
band, launched in 1978) but was only active for a matter of months before the435
satellite malfunctioned, although those data can be used for interferometry,436
including over volcanic terrains (Zebker and Villasenor, 1992). Short-term437
orbital radar experiments were also conducted in the 1980s and 1990s using438
NASA’s Space Shuttle as part of the Shuttle Imaging Radar (SIR) A, B,439
and C missions, which included a variety of wavelengths and good cover-440
age of volcanic targets (Gaddis et al., 1989; Zebker et al., 1996), although441
only SIR-C had the ability to measure topography and deformation. The442
first long-term, repeated SAR observations that could be applied to vol-443
cano research and monitoring began with the launch of the European Space444
Agency’s C-band ERS-1 SAR satellite in 1991, which subsequently led to445
the first published application of InSAR to volcano deformation, at Mount446
Etna (Massonnet et al., 1995). This mission was later joined by the ERS-2447
and ENVISAT satellites, which continued European Space Agency C-band448
monitoring of volcanoes for 20 years and formed the foundation of most vol-449
cano InSAR studies during the 1990s and 2000s (Figure 4). The launch450
of RADARSAT-1 in 1995 expanded this C-band catalog, with the excellent451
longevity of the satellite providing an especially valuable archive of data over452
the world’s volcanoes that could be exploited for studies of volcano defor-453
mation over decadal timescales (Baker and Amelung, 2012). RADARSAT-2,454
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active since 2007, has continued this legacy of C-band observations through455
to the present.456
The launch of the Japanese Aerospace Exploration Agency’s JERS-1457
satellite in 1992 contributed to the realization of the importance of L-band458
SAR data for volcano deformation monitoring, given the much greater coher-459
ence of longer wavelengths in vegetated areas (Lu et al., 2005b) (Figure 5).460
Unfortunately, JERS-1 suffered mechanical failures that limited the num-461
ber of potential interferograms that could be generated. It was not until462
the launch of ALOS-1 in 2006 that comprehensive L-band studies of volca-463
noes located in tropical and other heavily-vegetated environments could be464
attempted. Such work revealed many deforming volcanoes that might not465
have been detected using C-band data (e.g., Chaussard and Amelung, 2012;466
Ebmeier et al., 2013b; Biggs et al., 2014).467
Since 2007, X-band data have been available to study volcanoes thanks468
to the TerraSAR-X, TanDEM-X, and COSMO-SkyMED missions. These469
data typically have a higher spatial resolution than those from C- and L-470
band–sometimes better than 1 m–and more frequent repeat times (especially471
in the case of COSMO-SkyMed, which is made up of a constellation of 4472
satellites that follow the same orbital path around Earth) (Table 1). The473
improved spatial and temporal resolution of these SAR systems has enhanced474
the potential of SAR imagery for change detection (e.g., Richter et al., 2013)475
and places these sensors on par with optical imagery in terms of ground-pixel476
size (Sansosti et al., 2014). The next generation of SAR satellites, beginning477
with the C-band Sentinel-1 and L-band ALOS-2 platforms, will complement478
the existing X- and C-band sensors, offering a range of wavelengths and479
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repeat intervals that will prove invaluable for future SAR studies of active480
volcanism. As demonstrated by Lundgren et al. (2013) for a 4.5-day-long481
fissure eruption at Kılauea Volcano in 2011, a diversity of wavelengths and482
satellites, combined with frequent image acquisitions, is key to capturing483
rapidly evolving, dynamic volcanic processes–a capability that is now within484
reach thanks to the growing number of orbital SAR platforms.485
As the number of satellite SAR missions has increased, numbers of pub-486
lished volcano SAR studies have seen a commensurate rise with a step-like487
increase in 2010 (Figure 4). The same step is apparent in other fields (like488
tectonics, landslides, and subsidence), but occurs one year earlier, in 2009.489
While it is difficult to identify whether or not the delay of the step in volcanol-490
ogy is significant and, if so, what might have caused the delay, we speculate491
that it might indicate a need for additional efforts within the volcanologi-492
cal community to promote SAR volcano applications and educate volcano493
scientists in its use.494
Airborne SAR systems have provided a valuable complement to space-495
borne platforms, with NASA instruments supplying observations of volcanic496
landforms and eruptive activity starting in the 1980s (Zebker et al., 1987).497
Since the 1990s, airborne SARs that have seen extensive use are AIRSAR498
(e.g., Gaddis, 1992), TOPSAR (e.g., Rowland, 1996), and UAVSAR (e.g.,499
Lundgren et al., 2013). Only the latter was designed for repeat-pass interfer-500
ometry, and its flexibility in terms of rapid deployment and ability to remain501
on station for extended periods of time to monitor an evolving volcanic crisis502
make it a valuable tool for volcano surveillance. Many of these instruments,503
however, are cost-prohibitive for use in volcano research, and therefore have504
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had limited operational uses. Future developments in airborne SAR instru-505
mentation would benefit from close collaboration between scientists an engi-506
neers, who can build on these past successes to design instrumentation that is507
both capable and cost-effective, thus ensuring its broad application for years508
to come. Perhaps the ultimate volcano-monitoring radar system in terms509
of ability to make near-continuous observations of topographic and surface510
change is ground-based SAR. Although expensive and not widely deployed at511
present, ground-based radars have contributed to an improved understand-512
ing of activity at several volcanoes, especially Soufriere Hills Volcano (Wadge513
et al., 2005, 2008) and Stromboli (e.g., Casagli et al., 2009; Di Traglia et al.,514
2013; Intrieri et al., 2013; Nolesini et al., 2013). Future improvements in515
instrument design and reduction in cost will increase the applications of this516
implementation of SAR volcano monitoring.517
The diversity of wavelengths and repeat intervals that are currently, or518
soon will be, available from a variety of satellite SARs, coupled with air-519
borne UAVSAR and ground-based radar measurements, provides a suite of520
resources for all-weather, near-daily (and sometimes continuous) measure-521
ment of volcano deformation at sites around the world. The initial appli-522
cations of InSAR to volcanology in the 1990s required weeks to months for523
processing and interpretation. By the 2010s, SAR data from many systems524
are available with a latency of just a few hours in some cases (e.g., SAR data525
are commonly delivered to customers in under 90 minutes once they have526
been received at the Alaska Satellite Facility ground station (Meyer et al.,527
2014)), and data processing can be completed in minutes. SAR has there-528
fore grown from a purely research tool appropriate for retrospective analysis529
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of volcanic events to a research and monitoring tool that can contribute key530
insights during a volcanic crisis. In the sections that follow, we explore appli-531
cation of SAR data–especially space-based observations–for tracking volcanic532
activity both above and below ground.533
3. Mapping surface characteristics with SAR534
As detailed in the previous section, SAR signals have two components:535
amplitude, which measures the strength of the reflected signal, and phase,536
which includes information about the distance between the radar and the537
target. When the target is Earth’s surface, both components, as well as the538
coherence (see equation 1) between data acquired at different times, contain539
valuable information about the ground and have the ability to determine540
a range of attributes, including surface roughness, surface geometry, scat-541
tering properties, surface topography, electrical properties, and changes in542
these parameters. While SAR data are perhaps best known for mapping543
surface deformation using interferometric methods (discussed in section 4),544
the ability to quantify other surface characteristics is of equal importance,545
as these datasets constitute critical resources for assessment and monitoring546
of volcanic hazards.547
3.1. Amplitude548
The most important surface characteristics that control the strength of549
SAR backscatter are moisture content, roughness, and slope. Surfaces that550
are oriented towards the radar, rough on the scale of the radar wavelength,551
and/or moist will generally have stronger reflected returns that those that552
are not. On volcanoes, roughness and slope tend to be the most important553
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of these factors, and they define much of the variation in amplitude within a554
radar image (Gaddis et al., 1989). Changes in these parameters may provide555
evidence of volcanic activity, including emplacement of new deposits and de-556
struction of existing landforms. Because radar images can be acquired at557
night and during cloudy conditions, SAR has a decisive advantage over opti-558
cal and infrared sensors for monitoring volcanism. This application is perhaps559
best demonstrated by recent activity at Soufriere Hills Volcano (Montserrat),560
Eyjafjallajokull (Iceland), and Merapi (Indonesia).561
The 1995 - present eruption of Soufriere Hills Volcano has been char-562
acterized by the extrusion and destruction of a series of silicic lava domes563
(Wadge et al., 2010). Tracking changes in SAR amplitude over time has564
been a valuable tool for mapping pyroclastic deposits and the evolution of565
the lava dome. Comparison of high-resolution (∼2-m pixel size) TerraSAR-X566
images acquired before and just after an explosion in July 2008 revealed that567
the dome remained stable and was not in danger of collapse–key information568
for civil defense officials that would not have otherwise been available in the569
days following the explosion due to dense cloud cover during that time period570
(Wadge et al., 2011). Pyroclastic deposits were also mapped using changes571
in SAR amplitude over time, with differences in radar shadows in valleys572
between pre- and post-eruptive imagery used to calculate the thicknesses of573
pyroclastic material that had been emplaced in those valleys (Wadge et al.,574
2011). These data were crucial for improving estimations of the eruption rate575
and its temporal evolution.576
At Eyjafjallajokull, the evolution of eruptive vents and ice cauldrons dur-577
ing the initial stage of the summit explosive eruption in 2010, when cloud578
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and ash cover prevented visual observations, was tracked by a sequence of579
air- and space-borne SAR images (Hooper et al., 2012). The data were used580
to characterize meltwater generation that ultimately led to flooding far from581
the eruption site (Magnusson et al., 2012).582
Merapi experienced a “100-year” eruption in 2010 that threatened hun-583
dreds of thousands of residents on the flanks of the volcano (Surono et al.,584
2012). Interpretation of satellite SAR amplitude data (e.g., Figure 6) in585
near-real time allowed those observations to be combined with ground-based586
geological and geophysical results. The data made possible quantifications of587
dome growth rates and were essential in prompting warnings issued by local588
authorities that ultimately saved thousands of lives (Pallister et al., 2013).589
The amplitude of SAR images was also used to map pyroclastic deposits as-590
sociated with this event. The amplitude of co-polarized (HH) L-band radar591
data decreased where the valley-confined and overbank pyroclastic flow de-592
posits were emplaced (red area on figure 7 b). Reworked PDC deposits,593
the surge zone, and thick tephra deposits are characterized by an increase594
in ground-backscattering (blue area on figure 7 b). These patterns are not595
similar to those observed with X-band data from Montserrat (Wadge et al.,596
2011), demonstrating the importance of wavelength in backscattering prop-597
erties of volcanic (and probably other) deposits. Several airborne synthetic598
aperture radar systems and the Spaceborne Imaging Radar-C/X-Band Syn-599
thetic Aperture Radar (SIR-C/X-SAR) acquired data in L, C and X bands600
over a few active volcanoes. These data have shown that L band images give601
the best results for mapping lava flows and distinguishing multiple flow units602
(Schaber et al., 1980; Gaddis, 1992; MacKay and Mouginis-Mark, 1997).603
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To date, studies of surface characteristics in volcanic areas using ampli-604
tude measurements from satellite SAR have been mostly restricted to co-605
polarized data, in which the polarizations (either horizontal-H or vertical-606
V) of the transmitted and received data are the same (HH or VV). Stud-607
ies using airborne SAR sensors indicate that cross-polarized data (HV or608
VH) are a more effective discriminator of lava flows having different tex-609
tures and surface roughness (Zebker et al., 1987; Gaddis, 1992). Cross-610
polarization is not available on most satellite SARs with the exception of611
RADARSAT-2 (Table 1). That satellite offers a wide range of beam modes,612
resolutions, and polarizations, including fully polarimetric (HH, HV, VH,613
and VV). RADARSAT-2 images from Kılauea Volcano, Hawai‘i demonstrate614
the utility of cross-polarized data in the study of volcanic regions. For exam-615
ple, distinguishing ‘a‘a from pahoehoe lava flows is relatively straightforward616
in cross-polarized data, but the distinction is less clear in co-polarized im-617
agery (figure 8). Likewise, identification of active lava flows can be aided by618
cross-polarized data. Mapping the extent of lava flows at Kılauea requires619
costly and time-consuming field visits or cloud-free optical/thermal satellite620
imagery. Cross-polarized RADARSAT-2 data, however, are able to easily621
distinguish the active flows from the surrounding forest–a distinction that is622
not clear from co-polarized data (figure 9)–and are not constrained by time623
of day or weather. Future use of cross-polarized SAR to map volcanic and624
other surface features and how they change over time will be facilitated not625
only by continued RADARSAT-2 acquisitions, but also cross-polarization626
modes available on SAR systems carried by both the Sentinel-1 and ALOS-2627
satellites.628
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3.2. Coherence629
The reflected signal received by a SAR sensor is a function of the char-630
acteristics of the scatterers within a resolution cell on the ground. If the631
geometry of the scatterers changes between the times of two SAR acquisi-632
tions, the reflection from that resolution cell will not be correlated between633
the two images (low value of coherence as defined by equation 1). As an ex-634
ample, vegetated areas are commonly incoherent in SAR interferograms that635
span a given time period owing to rapid changes in the orientation of leaves636
and branches over time (although this can be mitigated to some extent by637
the use of longer wavelengths – particularly L-band; (Zebker and Villasenor,638
1992)). This lack of coherence in interferograms is typically regarded as a639
noise source in InSAR studies, since phase-difference information for defor-640
mation cannot be retrieved from incoherent regions, but there are important641
applications of coherence mapping in Earth science (Zebker and Villasenor,642
1992).643
In volcanology, incoherence can be caused by deposition of pyroclastic644
material, lahars, and lava, and by extreme deformation of the ground sur-645
face. As a result, maps of coherence that span eruptive activity may indicate646
areas covered by volcanic deposits or severely deformed (see Figure 7 c). At647
Unzen volcano, Japan, Terunuma et al. (2005) demonstrated the utility of648
coherence maps for delineating pyroclastic flows and lahars, and McAlpin649
and Meyer (2013) utilized coherence to map lahar deposits emplaced dur-650
ing the 2009 eruption of Redoubt volcano, Alaska. Similarly, lava-flow area651
can be mapped by means of SAR coherence, as demonstrated in Hawai‘i652
(Zebker et al., 1996) and the Galapagos (Rowland et al., 2003) as long as653
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the lava flows are confined to previously coherent areas (in other words, not654
traversing vegetated areas). By combining lava-flow area determined from655
coherence with estimated flow thickness, it is possible to calculate the aver-656
age effusion rate of lava over the time spanned (Zebker et al., 1996; Poland,657
2014). Dietterich et al. (2012) extended the application of lava-flow mapping658
with coherence by developing a method for combining results from different659
satellites and look angles–datasets that are typically treated independently660
in deformation studies. Using 211 scenes from 6 ENVISAT tracks, they were661
able to map lava-flow activity at Kılauea with a temporal separation between662
coherence imagery of as little as 1 day, and on average less than 2 weeks (fig-663
ure 10A). The exceptional all-weather, day/night ability to map active areas664
over the entirety of Kılauea’s > 100 km2 lava flow field–all with high temporal665
and spatial resolution but without requiring ground-based equipment or field666
personnel–represents a quantum leap in tracking of lava flow emplacement.667
Not only are such data critical to tracking the hazard due to lava flows, they668
can also be used to constrain flow thickness (based on the time after emplace-669
ment for a new lava flow to become coherent: Dietterich et al. (2012))(figure670
10B) and therefore effusion rate–variables of obvious importance to volcano671
monitoring.672
3.3. Phase used for topographic measurement673
While phase differences in SAR scenes are most commonly used to de-674
termine surface deformation, the data can also be used to calculate surface675
elevations (see section 2.3)–one of the most important datasets in volcanol-676
ogy. For example, topographic information is critical input for models that677
forecast flow paths, particularly for lava (Harris and Rowland, 2001; Favalli678
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et al., 2011) and lahars (Iverson et al., 1998), and it forms the base for most679
geologic mapping. Especially in flow-path applications, topographic data680
should have resolution that is sufficient to accurately forecast paths, and it681
should be updated frequently to account for changes that may impact the682
flow direction of lava and lahars as volcanic activity progresses. In addition,683
up-to-date topographic information is required for mapping surface deforma-684
tion using SAR (section 2.4.1).685
Topographic information is easily obtained from SAR data and, in fact,686
is the source of much current global topographic information (see Lu et al.687
(2012), for a review). Three methods are commonly used to extract ele-688
vation data from SAR phase: 1) repeat-pass measurements using a single689
radar instrument, 2) examination of topographic artifacts in deformation in-690
terferograms, and 3) single-pass measurements using two radar instruments691
concurrently.692
Repeat pass methods utilize data collected of the same point on the693
ground from about the same place in the air or space at two different times.694
Repeat-pass ERS-1/2 data produced some of the first space-based DEMs of695
volcanoes, including at Okmok, Alaska (Lu et al., 2003), and in the Galapagos696
(Rowland et al., 2003). Comparison between SAR-derived DEMs acquired697
before and after eruptions at those volcanoes revealed the volume of sub-698
aerial lava accumulation. The repeat-pass method requires knowledge of the699
baseline between the SAR systems at the times of image acquisition, since700
sensitivity to topography is directly proportional to baseline length (see equa-701
tion 3). Uncertainty is introduced by imprecisely known baselines (Zebker702
and Goldstein, 1986; Farr et al., 2007) and atmospheric conditions varying703
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between acquisitions (Zebker et al., 1997), and no topographic information704
can be retrieved in areas that are incoherent between SAR acquisitions (Ze-705
bker and Villasenor, 1992). The coherence problem was mitigated somewhat706
by the tandem ERS-1/2 mission, when the orbits of the two satellites were707
configured such that acquisitions of the same area on Earth could be made708
with a temporal separation of 1 day, thus reducing the effects of temporal709
decorrelation.710
A variation on the repeat-pass method is to determine elevation from711
an examination of topographic artifacts in interferometric phase data. When712
processing interferograms to characterize surface deformation, the phase con-713
tribution from topography is removed using a preexisting DEM (see section714
2.4.1); any topographic change that occurred since the acquisition of the715
DEM will be manifested as residual phase. Using the baseline length, this716
phase can be converted to elevation change (through equation 3) and added717
to the preexisting DEM to derive an updated topographic map of the region.718
The use of a large number of interferograms can mitigate potential errors719
due to atmospheric artifacts. Ebmeier et al. (2012) were able to estimate720
lava flow thickness with an uncertainty of around 9 m using a minimum of 5721
interferograms that had large baselines (and, therefore, improved sensitivity722
to topography). As with repeat-pass DEM generation, however, topographic723
information in the interferogram is only retrievable where the interferogram724
is coherent. Nevertheless, this method provides a useful means of not only as-725
sessing deformation, but also solving for surface elevation changes over time726
that may not be represented in the initial DEM.727
Single-pass interferometry is by far the most efficient method for utilizing728
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SAR to map surface elevations (Zebker and Goldstein, 1986). The technique729
uses two SAR sensors separated by an appropriate distance to simultaneously730
record the radar signal reflected from the surface, thus eliminating incoher-731
ence due to changes in the scattering properties of the surface over time and732
artifacts due to temporal variations in atmospheric conditions. The airborne733
TOPSAR (TOPographic SAR) system is one example that has been used734
to establish both pre- and post-eruption topography in volcanic areas (Lu735
et al., 2003; Rowland et al., 2003). Perhaps the most well-known use of736
the single-pass technique is the Shuttle Radar Topography Mission (SRTM),737
which was flown on the Space Shuttle in 2000. The mission recorded reflected738
radar signals on two SAR antennas on either end of a 60-m-long mast, the739
fixed distance of which helped to reduce uncertainty in the derived elevation740
data due to improper knowledge of the baseline (Farr et al., 2007). SRTM741
data are commonly used to remove topographic phase from deformation in-742
terferograms and have been an invaluable contribution to Earth science in743
general, but users should beware that topographic change since 2000 will744
not be represented in SRTM DEMs and will be manifested as residual phase745
that might be incorrectly interpreted as deformation. In such cases, users746
should attempt to obtain a more current DEM for their study area or employ747
a means of simultaneously solving for deformation and topographic change748
over time.749
Following the SRTM mission, it was over a decade before another satellite750
system was able to acquire SAR data in single-pass mode. The TanDEM-X751
mission of the German Space Agency consists of two nearly identical X-band752
SAR satellites that orbit in close proximity, separated only by about 200753
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m. In bistatic mode, one satellite transmits a radar pulse to the surface754
and both receive the reflected signal. Applied to volcanoes, topographic755
information derived from TanDEM-X data has been used to construct post-756
eruptive DEMs that have documented volumes of both lava accumulation (Xu757
and Jonsson, 2014) and dome collapse (Kubanek et al., 2014a,b). At Kılauea,758
where lava effusion has been nearly continuous from vents on the volcano’s759
East Rift Zone since 1983, a time series of DEMs derived from TanDEM-X760
data was used to map the 4-dimensional evolution of the lava flow field (figure761
11a ) (Poland, 2014). Such data can be used to calculate the subaerial effusion762
rate of lava over time (figure 11b)–a parameter that may only be poorly763
estimated using other ground-based or remote-sensing techniques. SRTM764
and TanDEM-X data demonstrate the utility and importance of single-pass765
satellite InSAR for deriving Earth topography–a foundation for many Earth766
science datasets and especially important in volcanology. Future satellite767
SARmissions should attempt to incorporate single-pass InSAR to address the768
need for up-to-date high-quality topographic information, which is necessary769
when trying to retrieve displacement evolution through time on a volcano770
marked by significant topographic changes.771
4. Insights from SAR into volcano deformation772
In the previous section, we detailed how SAR imagery can distinguish773
changes in the surface characteristics of volcanoes over time by quantifying774
eruptive deposits and topographic evolution. The most common application775
of SAR to volcanology, however, remains deformation measurement. In this776
section, we first review the various phenomena that induce surface displace-777
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ments in volcanic areas together with the models developed to interpret this778
deformation. We then discuss how InSAR has helped to understand vol-779
canic processes, like magma storage and transport, subsidence of volcanic780
deposits, and evolution of volcano deformation over time, before discussing781
the limitations of InSAR for volcano geodesy.782
4.1. Sources of deformation around volcanoes783
There are several potential sources of deformation in volcanic areas. Most784
of these sources are related to magmatic activity, but volcanoes are often also785
subject to tectonic deformation, and volcanic edifices can be subject to land-786
slides. In this section we focus on deformation associated with magmatism787
and volcanic eruptions. For additional information on volcano deformation788
modeling see, e.g., Dzurisin (2007) or Segall (2010).789
790
In volcanic areas, the first deformation source to be identified and in-791
terpreted though modeling was inflation/deflation induced by a localized792
magmatic storage zone at depth. Inflation is due to pressure increase by793
magma inflow or crystallization (e.g., Tait et al., 1989), while deflation can794
be caused by magma withdrawal (either to deeper levels or to feed a nearby795
eruption), thermal contraction or gas loss. The simplest model to interpret796
such a signal was proposed by Mogi (1958), who applied it to explain leveling797
and triangulation data collected at Kılauea, Hawai‘i, and Sakurajima, Japan.798
The “Mogi” model is still often applied and gives the surface displacement799
induced by an overpressurized point-source embedded in an elastic homoge-800
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nous and isotropic half-space. Vertical displacement is expressed by:801
Uz(z = O, r) =∆PcR
3
c
G(1− ν)
Hc
(H2c + r2)3/2
, (6)
where Hc and Rc are, respectively, the depth and the radius of the magma802
chamber, G and ν characterize the elastic crustal behavior (respectively, shear803
modulus and Poisson’s ratio), ∆Pc is the overpressure, and r is the radial804
distance at the surface from the axis of symmetry (a vertical axis though the805
center of the magma reservoir). The induced vertical displacement is thus806
at a maximum directly over the centre of the magma chamber and decreases807
monotonically in all directions. This formulation is valid only for reservoirs808
that are small in size compared to their depth, and does not permit determi-809
nation of the reservoir size. McTigue (1987) showed that, even considering a810
finite source, it was almost impossible to distinguish between a highly pres-811
surized source of small extent and a marginally pressurized larger source. To812
get around this ambiguity, the source amplitude is often described in terms813
of a volume change, ∆Vin, where814
∆Vsurf
∆Vin
=2(1− ν)
1 + 4G3K
, (7)
withK being the effective bulk modulus for the stored magma. Note that the815
compressibility of magma can accommodate a certain degree of magma ac-816
cumulation or withdrawal without resulting in surface deformation (Johnson817
et al., 2000; Rivalta and Segall, 2008).818
Other analytical solutions have been proposed to account for an ellipsoidal819
reservoir (Yang et al., 1988; Newman et al., 2006) or a horizontal crack that is820
circular in plan view (Fialko et al., 2001). Improved analytical solutions also821
allow for the effects of topography (McTigue and Mei, 1981; Williams and822
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Wadge, 2000) and viscous behaviour around the magmatic reservoir (e.g.,823
Dragoni and Magnanensi, 1989) to be taken into account. The use of nu-824
merical methods makes possible more complex source geometries, material825
rheologies and properties, considering, for instance, crustal layering (Currenti826
et al., 2010; Pearse and Fialko, 2010; Got et al., 2013).827
Magma storage at shallow depths can persist for months to years (e.g.,828
Sturkell et al., 2006; Elsworth et al., 2008), with any associated inflation rep-829
resenting a potential eruption precursor (Dzurisin, 2003). The rate of pres-830
surization often decreases exponentially, which can be interpreted as magma831
replenishment from a deep constant pressure source (e.g., Lengline et al.,832
2008) or reservoir (e.g., Reverso et al., 2014). At some point, the overpres-833
sure within a shallow reservoir may reach a critical state, leading to rupture834
of the reservoir walls and magma migration, which may eventually feed an835
eruption.836
837
When magma starts migrating, it propagates though the crust as pla-838
nar or curviplanar features called dikes or sills. Sills are usually horizontal839
structures, whereas dykes are steeply inclined or vertical. Intrusion path840
and velocity depend on the driving overpressure of the magma and the local841
stress field, as well as the physical properties of both the magma (primarily842
density and viscosity) and the surrounding crust (primarily density, elastic843
properties and fracture toughness) (Lister and Kerr, 1991; Maccaferri et al.,844
2011). This migration is usually a short-term phenomenon, lasting for hours845
to days. Intrusions can reach the surface or remain stalled at depth. Even in846
the case of eruption, a given amount of magma may remain trapped at depth,847
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thus inducing a co-eruptive displacement field. The model most commonly848
used to interpret the deformation field resulting from a magmatic intrusion849
is the so-called “Okada” model (Okada, 1985), which gives the deformation850
field produced at the surface by a finite displacement applied on a rectan-851
gular dislocation in an elastic, homogeneous and isotropic half-space. This852
model can be discretized into a number of smaller rectangular elements to853
obtain a distribution of displacement over the plane. Numerical modeling854
can also be applied to constrain the stress distribution along the dislocation855
surface.856
857
In the case of andesitic volcanoes, curviplanar magmatic intrusions of-858
ten link at shallow levels to open conduits, which feed effusive and explosive859
summit eruptions (e.g., Costa et al., 2007). Viscous magma flow through860
open conduits induces both pressurization and shear forces causing near-861
field displacements in the vicinity of the summit (Beauducel et al., 2000;862
Green et al., 2006). Analytical solutions have been proposed to account for863
both pressurization (Bonaccorso and Davis, 1999) and shear stress (Anderson864
et al., 2010), and numerical models allow for investigation of the full coupling865
between magma flow and surface deformation (Albino et al., 2011; Anderson866
and Segall, 2011).867
868
Emplacement of magmatic or volcanic material, either as intrusions or869
eruptive deposits at the surface, contributes to volcanic edifice construction.870
Under the influence of magmatic forcing, local tectonic stresses, gravity and871
climatic effects, a volcanic edifice undergoes surface deformation. Summit872
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extension in association with compressive structures at the base of an ed-873
ifice have been interpreted as spreading of a volcano under its own weight874
(Borgia, 1994). Large-scale flank sliding has also been identified as an im-875
portant feature of large volcanic edifices, especially for oceanic volcanoes,876
and has been addressed by stability studies taking into account the effect877
of magma accumulation at depth (e.g., Iverson, 1995; Apuani et al., 2005;878
Chaput et al., 2014). On a smaller spatial scale, eruptive deposits are sub-879
ject to compaction and also act as a surface load, inducing local subsidence880
(Beauducel et al., 2000). These loads can be analytically quantified by sum-881
mation of the Green’s function for the response of a point load on an elastic882
half-space (Grapenthin et al., 2010).883
884
At many volcanic areas, the interplay between shallow meteoric water885
and magma inflow leads to the development of active hydrothermal systems886
that are regularly perturbed by the injection of hot fluids. The evolution of887
hydrothermal systems can be manifested in deformation at volcanoes, and888
microgravity monitoring (e.g., Battaglia et al., 1999) and numerical models889
can help to distinguish between magmatic and hydrothermal activity (Hur-890
witz et al., 2007; Hutnak et al., 2009; Fournier and Chardot, 2012).891
892
As detailed above, magmatic activity can produce deformation in many893
different ways. Deformation certainly does not occur only during eruptive cri-894
sis, but may last for years before and after an eruption. From the perspective895
of hazard assessment, a key requirement is the ability to distinguish deforma-896
tion signals induced by magmatic activity from those caused by external phe-897
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nomena, such as uplift induced by ice retreat for subglacial volcanoes (e.g.,898
Pinel et al., 2007). A further issue is to determine to what extent magma899
storage or migration is occurring, and whether magma migration will lead to900
eruption. In addition to other useful observations like gravity measurements,901
gas flux, seismicity or ground-based geodetic records, high-spatial-resolution902
displacement data from InSAR can make an obvious contribution to these903
problems. Another important goal of deformation studies, in addition to904
eruption forecasting, is to improve knowledge of the geometry and behaviour905
of magma plumbing systems, including changes in the associated stress field.906
Here, too, the contribution from InSAR can be of great benefit.907
4.2. Overview of volcano deformation studies based on SAR data908
As of 2014, InSAR has revealed deformation at more than 160 volcanoes909
around the world (Biggs et al., 2014), including all types of source processes910
discussed in section 4.1 (Figure 12; near-field deformation related to viscous911
magma flow in conduits was a potential exception but recently Salzer et al.912
(2014), taking advantage of the high spatial and temporal resolution provided913
by TerraSAR-X data, observed transient deformation induced at very shallow914
depth beneath the summit dome of Colima volcano that may be related to915
conduit flow). The resulting databases of volcano deformation (e.g., Fournier916
et al., 2010; Biggs et al., 2014) have demonstrated the strong link between917
deformation and eruption, although – just as importantly – not all volcanoes918
that erupt show signs of precursory, or even co-eruptive, deformation (e.g.,919
Moran et al., 2006; Chaussard et al., 2013; Ebmeier et al., 2013b; Biggs et al.,920
2014). Over 20 years of satellite SAR data have facilitated construction of921
time series to investigate the temporal evolution of volcano deformation and922
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have even enabled observation of multiple eruption cycles at some volcanoes923
(e.g., Bagnardi et al., 2013; Wauthier et al., 2013). In addition, InSAR924
provides an opportunity to image exceptional volcanic events – for example,925
the caldera collapse, voluminous eruption, and associated flank slip at Piton926
de la Fournaise volcano, Reunion Island, in April 2007 (Froger et al., 2010;927
Clarke et al., 2013). Even more striking is the 2005 rifting event in Afar,928
Africa, which was associated with emplacement of a 65-km-long dike with a929
volume in excess of 1 km3 (Wright et al., 2006; Grandin et al., 2009) (see930
figure 13) and was followed in subsequent years by 13 additional intrusive931
events (Grandin et al., 2010; Hamling et al., 2010).932
A complete review of the numerous studies that have utilized InSAR to933
investigate volcano deformation is impossible owing to the ever-growing ap-934
plication of the technology (Figure 4) – a sure sign of its maturation. Instead,935
we discuss here the primary contributions of InSAR to volcano dynamics –936
specifically magma storage (section 4.2.1), magma transport (section 4.2.2),937
deposit subsidence (section 4.2.4) and temporal evolution of deformation938
(section 4.2.3) –while highlighting a few, but by no means all, examples939
of these processes as investigated by InSAR..940
4.2.1. Magma storage941
As documented previously, one of the great advantages of InSAR over942
other deformation-monitoring methods is the ability to scan entire volcanic943
arcs for localizations of cm-scale displacements – especially valuable in ar-944
eas where in-situ monitoring is limited or nonexistent (e.g., Amelung et al.,945
2000; Pritchard and Simons, 2002, 2004; Biggs et al., 2011; Henderson and946
Pritchard, 2013; Lu and Dzurisin, 2014). For the 1990s and most of the947
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2000s, only C-band data were available, limiting arc-wide surveys to poorly-948
vegetated regions (Pritchard and Simons, 2002, 2004). L-band studies using949
the ALOS satellite starting in 2006 expanded this capability to tropical ar-950
eas (e.g., Fournier et al., 2010; Ebmeier et al., 2011; Philibosian and Simons,951
2012; Chaussard and Amelung, 2012; Chaussard et al., 2013; Ebmeier et al.,952
2013a,b), allowing for near-global remote mapping of volcano deformation.953
Such regional studies have recognized unrest at supposedly quiescent volca-954
noes and aided the construction and refinement of databases and catalogs of955
volcano characteristics. For example, based on the tables provided by Biggs956
et al. (2014), it is possible to ascertain global patterns of magma storage957
depth, which reveal that most subvolcanic magma storage is within 10 km958
of the surface (Figure 14), although this observation might be partly biased959
by the difficulty in imaging subtle deformation from deeper sources. Such960
catalogs facilitate investigations of the parameters that control magma stor-961
age. Chaussard and Amelung (2014) analyzed magma storage depth (based,962
in part, on InSAR results) at 70 volcanoes worldwide with respect to both963
crustal structure and stress regime, finding that magma reservoirs tend to be964
deeper where crust is older, thicker, and subject to compressive stress.965
The broad coverage of SAR provides an opportunity to image large-scale966
deformation due to deep magma storage (Figure 14) – a perspective that967
is not easily achievable using ground-based point measurements. Leveling968
data over the Socorro magma body in New Mexico were used to identify969
several mm/yr uplift associated with a low-seismic-velocity region at a depth970
of 20 km, but only vertical displacements were available, and only from971
a few transects over the deforming zone (Reilinger et al., 1980). InSAR,972
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however, provided the improved temporal and spatial resolution needed to973
refine models of deformation (Fialko and Simons, 2001) and interpret the974
displacements in terms of heat transfer from a stalled magmatic intrusion975
(Pearse and Fialko, 2010). Areas of deep magma accumulation have also976
been identified using InSAR in South America and Iceland – in some cases,977
deformation that would not otherwise have been discovered. The pattern978
of uplift at Uturuncu, in Bolivia, is consistent with magma accumulation979
at 20-km depth within the Altiplano-Puna province – an area of intense980
silicic volcanism over the past 10 million years that is apparently still active981
(Pritchard and Simons, 2002, 2004; Sparks et al., 2008; Fialko and Pearse,982
2012; Henderson and Pritchard, 2013; Walter and Motagh, 2014). In Iceland,983
deep magma accumulation at 20-km depth has been recognized at Krafla984
(de Zeeuw-van Dalfsen et al., 2004), Hekla (Ofeigsson et al., 2011), and even985
as part of a dipping dike in the Northern Volcanic Zone (Hooper et al., 2011).986
InSAR has also helped to identify and refine the geometrical character-987
istics of magma storage, especially given that ground-based data are some-988
times insufficient to distinguish between spherical and more complex source989
shapes. Taking the Socorro example, InSAR data not only provided evi-990
dence the magma storage zone had a sill-like geometry (Fialko and Simons,991
2001), but also motivated the development of a new analytical solution for992
a circular penny-shaped crack (Fialko et al., 2001) that has seen extensive993
use in volcano deformation modeling (e.g., Baker and Amelung, 2012). In994
Hawai‘i at Mauna Loa – the largest active volcano by volume on Earth –995
inflation and deflation measured by tilt, Electronic Distance Measurement,996
and campaign GPS during the 1970s through 1990s suggested a spherical997
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magma storage area at 3-4 km beneath the south part of the caldera (Lock-998
wood et al., 1987). InSAR measurements and expanded application of GPS,999
however, demonstrated that magma storage occurred not only beneath the1000
south caldera, but also in a dike-like structure that underlies and follows1001
the trend of the elongated caldera (Amelung et al., 2007). Deformation at1002
Yellowstone caldera, Wyoming, was mostly characterized by leveling mea-1003
surements for decades, which yielded an exceptional record of the ups and1004
downs of the active caldera system (Dzurisin et al., 1994) but was restricted1005
to, generally, a single transect across the eastern part of the caldera. InSAR1006
results demonstrated, for the first time, the outstanding complexity of de-1007
formation, which included multiple sources within and outside of the caldera1008
(Wicks et al., 1998, 2006; Chang et al., 2007, 2010). Finally, the number of1009
magma reservoirs, while possible to infer from GPS, leveling, tilt, and strain1010
monitoring, can be determined unequivocally by InSAR, as experience at1011
Kılauea demonstrates. While multiple storage areas have been hinted at1012
for decades by ground-based displacement data (Fiske and Kinoshita, 1969;1013
Miklius and Cervelli, 2003), InSAR results clearly delineated multiple, dis-1014
crete magma storage areas beneath the volcano’s summit region (Baker and1015
Amelung, 2012).1016
Even where only a single magma storage area is present, ground-based1017
monitoring may not be positioned in the best location to detect deformation1018
due to magma accumulation or withdrawal. Near South Sister, Oregon, for1019
example, InSAR detected inflation centered 5 km west of the volcano’s sum-1020
mit (Figure 15 ); it is not clear if the deformation would have been recognized1021
solely from in-situ measurements, which are traditionally centered on volcano1022
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summits, even though the center of deformation may be far removed (Wicks1023
et al., 2002; Pritchard and Simons, 2004; Dzurisin et al., 2006). These exam-1024
ples provide clear evidence of the power of InSAR to not only detect magma1025
storage at a variety of locations and depth ranges, but also to elucidate the1026
geometry of magma storage, both in terms of reservoir shapes and numbers.1027
4.2.2. Magma transport1028
From storage areas, how does magma get to the surface? Seismicity pro-1029
vides obvious constraints on the process (e.g., Taisne et al., 2011), and defor-1030
mation data bracketing, for instance, a dike intrusion event can place bounds1031
on the volume of magma transport and also reveal the geometry of a volcano’s1032
magma plumbing system. The relatively poor temporal resolution of SAR1033
data (see section 4.3) limits its ability to catch magma transport ”in the act”,1034
which is usually much better imaged by high-rate GPS or tilt data (e.g., Aoki1035
et al., 1999). However, the literature is replete with exceptional examples of1036
InSAR as a tool for mapping magma transport pathways at volcanoes of1037
all tectonic settings and compositions (see Table 2). At Yellowstone, Wicks1038
et al. (2006) used InSAR data to reveal a complex pattern of surface displace-1039
ments over time, which they used to infer how magma enters, traverses, and1040
leaves the caldera system. InSAR-derived surface displacements associated1041
with the rhyolitic eruption of Chaiten volcano, Chile, beginning in 2008, not1042
only indicated the dike-like pathway that magma used to get to the surface,1043
but also showed that storage and segregation of low-density liquid were con-1044
trolled by existing faults (Wicks et al., 2011). On the lower-silica end of the1045
compositional spectrum, repeated intrusions detected by InSAR at Eyjaf-1046
jallajokull, Iceland (Pedersen and Sigmundsson, 2004, 2006), culminated in1047
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2010 with an effusive basaltic eruption from flank vents followed about three1048
weeks later by an explosive andesitic eruption from the summit (Sigmunds-1049
son et al., 2010). Deformation from InSAR and, to a lesser extent, GPS1050
over the two decades before the 2010 eruptive activity revealed the plumbing1051
system of the volcano, which allowed for interpretation of other datasets,1052
such as seismicity and petrology (Sigmundsson et al., 2010). At Fernandina,1053
in the Galapagos, intrusion of sills several km beneath the flanks of the vol-1054
cano would not have been detected without InSAR. The recognition that sills1055
can intrude from subvolcanic storage areas may explain several exceptional1056
episodes of rapid uplift of coastal areas in the archipelago that occurred in1057
the early-mid 20th century (Bagnardi and Amelung, 2012). Thanks to the1058
improved understanding of the geometry and volume of magmatic intrusions1059
provided, in part, by models of InSAR data, it is now possible to quantify1060
the stress-field perturbation induced by magma emplacement at depth (e.g.,1061
Amelung et al., 2007; Hamling et al., 2009; Grandin et al., 2010; Bagnardi1062
et al., 2013; Biggs et al., 2013). The existing stress field may also affect1063
intruding dikes. A study of the Upptyppingar intrusion in northern Iceland1064
demonstrated that the dike was at an angle to the least compressive stress,1065
which induced a shear component to the dike opening (Hooper et al., 2011).1066
In this case, inversion of the deformation field using numerical methods pro-1067
vided a means to constrain the background stress field at the time of magma1068
emplacement (Figure 16).1069
Characterizing active magma transport via deformation measurements is1070
typically the domain of continuous, ground-based sensors, which can track1071
magma migration on the same time scales over which it occurs – generally1072
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minutes to days (e.g., Montgomery-Brown et al., 2011). Catching the process1073
of magma transport “in the act” with InSAR, rather than bracketing an1074
event, requires serendipitous data acquisitions or long-lived transient activity,1075
but can yield exceptional results. For example, a SAR image acquired two1076
hours prior to the onset of the 2009 eruption of Fernandina revealed the1077
initial stages of sill propagation away from the subvolcanic magma reservoir1078
towards the surface (Figure 17). Those data not only aided interpretation1079
of that eruption, but also suggested the mechanism behind the pattern of1080
circumferential and radial eruptive fissures common to all western Galapagos1081
volcanoes – a problem that had vexed geologists for several decades (Bagnardi1082
et al., 2013). The March 5-9, 2011, Kamoamoa fissure eruption at Kılauea1083
Volcano lasted sufficiently long to be imaged by several SAR satellites over1084
the course of the activity, and models of derived interferograms revealed1085
progressive dike opening and volume increase over time (Lundgren et al.,1086
2013). Multiple SAR satellites were also used to track deformation associated1087
with lateral magma migration over the course of several months prior to1088
the 2011 offshore eruption of El Hierro, Canary Islands, demonstrating that1089
InSAR can even provide constraints on magma plumbing associated with1090
eruptive activity that cannot be observed directly (Gonzalez et al., 2013).1091
The capability of InSAR to image deformation associated magma migra-1092
tion that culminates in both eruptions and non-eruptive intrusions will only1093
grow with the launch of new satellites and continued operation of existing1094
systems. As of mid-2014, five orbital SAR systems were available for sci-1095
entific applications (Table 1) – TerraSAR-X/TanDEM-X, COSMO-SkyMed,1096
RADARSAT-2, Sentinel-1a, and ALOS-2. Given that some of these systems1097
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include multiple satellites and have short repeat times (on the order of days),1098
and that they can be complemented in a few locations by repeated airborne1099
SAR acquisitions, the potential for imaging magma transport ”in the act”1100
with high spatial resolution is better than ever. Such data will provide new1101
input for models of magma transport that will aid in the development of mon-1102
itoring strategies and modeling approaches (Segall, 2013) and may reveal new1103
insights into the structure and dynamics of active volcanism (Bagnardi et al.,1104
2013).1105
4.2.3. Temporal evolution of magmatic deformation1106
More than two decades of InSAR have enabled not only detection of1107
volcano deformation at locations around the world, but also compilation of1108
deformation time series that have made possible an abundance of modeling1109
studies and revealed feedback patterns between various types of volcanic and1110
tectonic activity. Fernandina provides a spectacular example, with InSAR1111
data extending back to 1992. Although there were few acquisitions in the1112
1990s, frequent collects in the 2000s captured deflation due to multiple in-1113
trusions and eruptions superimposed on a background trend of inflation that1114
is measured in terms of meters (Figure 20). Dense time series have also been1115
used to characterize the onset and evolution of deformation at volcanoes1116
that had been quiescent, like South Sister, which began inflating in 19961117
(Dzurisin et al., 2006, 2009; Riddick and Schmidt, 2011), and Laguna del1118
Maule, Chile, which experienced high-rates of deformation during 2007-20121119
that may be a consequence of processes within a large silicic magma chamber1120
(Feigl et al., 2014). Further, high-spatial-resolution deformation time series1121
provide critical input to models of magma ascent and accumulation that go1122
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beyond simple elastic approximations like those of Mogi (1958) and Okada1123
(1985). For example, deformation time series from Socorro indicate steady1124
uplift, but modeling argues against constant magma overpressure (the con-1125
clusion based on elastic models) and instead in favor of heat transfer and1126
ductile deformation above a giant, previously emplaced sill intrusion (Pearse1127
and Fialko, 2010). Tracking post-eruptive volcano inflation has provided an1128
indication of the replenishment of shallow reservoirs (Lu et al., 2010), which1129
places constraints on magma viscosity and connections with deep magma1130
sources.1131
Perhaps most importantly, temporally dense, high-spatial-resolution In-1132
SAR data supply information regarding interactions between different vol-1133
canic process, and between volcanic and tectonic activity. An outstanding ex-1134
ample is the response of volcanoes in Japan and South America to large earth-1135
quakes. Within days of the 2010 Mw8.8 Maule earthquake off the coast of1136
Chile, subsidence of up to 15 cm was detected at 5 volcanoes in the southern1137
Andes, attributed to coseismic hydrothermal fluid release (Pritchard et al.,1138
2013). Similarly, subsidence was detected at several volcanoes in Japan fol-1139
lowing the 2011 Mw9.0 Tohoku earthquake, possibly indicating deformation1140
of thermally-weakened areas around large magma chambers due to coseismic1141
stress changes (Ozawa and Fujita, 2013; Takada and Fukushima, 2013). It is1142
doubtful that the full extent of either of these earthquake-triggered deforma-1143
tion episodes would have been discerned without InSAR results. As always,1144
the information provided by InSAR should be interpreted together with other1145
geophysical datasets, especially seismic noise studies, which reveal a seismic1146
velocity drop localized below volcanic regions after the Tohoku earthquake1147
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(Brenguier et al., 2014). InSAR data have also documented flank displace-1148
ments that have occurred as a consequence of eruptive and intrusive activity.1149
Etna has experienced multiple such episodes, with InSAR time series sug-1150
gesting that dike emplacement in 2001-2002 triggered asymmetric motion of1151
the volcano’s eastern flank (Solaro et al., 2011), while an episode of flank1152
motion in 2008 might have provided the motivation for a subsequent dike1153
intrusion (Bonforte et al., 2013). At Piton de la Fournaise, InSAR recorded1154
a complex interplay between co-eruptive deflation, dike intrusion, and flank1155
motion associated with caldera collapse in 2007 – the first well-documented1156
evidence for flank instability at that volcano (Clarke et al., 2013).1157
Ultimately, the importance of temporally dense, high-spatial-resolution1158
deformation maps from InSAR is the ability to combine those data with1159
other observations to better understand how volcanoes work – especially1160
relevant for investigating the mechanisms of large-scale eruptions that can1161
have global impact. Santorini, for instance, is well known as the source of1162
an eruption that might have played a major role in the devastation of the1163
Minoan civilization ∼3600 years ago, and uplift that began there in 20111164
served as a reminder that the volcano is still active. Considering the infla-1165
tion in light of the petrology of past eruptive products, however, argues that1166
the shallow, geodetically imaged storage area is less important in terms of1167
controlling eruptive activity than the deeper magmatic system (Parks et al.,1168
2011). InSAR-derived deformation also provides outstanding constraints on1169
magma supply to a given volcano (Poland et al., 2012), which is the dominant1170
control on the volumes and timings of surface eruptive activity. Improvement1171
of the temporal resolution of geodetic observations, showing for instance a1172
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period of transient subsidence not associated with an eruptive crisis, has also1173
revealed the need to consider the petrological effects related to degassing and1174
crystallization in order to correctly interpret temporal evolution of displace-1175
ments recorded at the surface above a magmatic reservoir (Caricchi et al.,1176
2014). As the number of SAR satellites and their ability to image defor-1177
mation globally continues to grow, so too will the incorporation of InSAR1178
time series in models of volcano evolution and eruptive activity. These data1179
will eventually feed in to dynamic models that account for magma migra-1180
tion through a volcanic plumbing system, as proposed by Melnik and Costa1181
(2013), which have potential for eruption forecasting, especially when they1182
incorporate deformation data.1183
4.2.4. Subsidence of volcanic deposits1184
While SAR interferometry is rightly known for its use in measuring surface1185
displacements associated with magma accumulation/withdrawal, magma mi-1186
gration, and fault slip at volcanoes, an underappreciated capability is charac-1187
terization of post-emplacement behavior in lava flows and pyroclastic units.1188
That such deposits deform after they are erupted is well known. For exam-1189
ple, both subsidence and uplift –attributed to lava cooling/crystallization and1190
vesiculation, respectively – have been measured on the surfaces of solidifying1191
lava lakes at Kılauea Volcano (Wright et al., 1976; Wright and Okamura,1192
1977; Peck, 1978). These dominantly vertical displacements, at Kılauea and1193
elsewhere, were initially documented by time- and personnel-intensive lev-1194
eling surveys (Murray and Guest, 1982). InSAR offers better spatial and1195
temporal resolution (assuming data are acquired during regular orbital re-1196
peats), and therefore provides improved insights into deformation patterns1197
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and source mechanisms.1198
The first InSAR observations of post-emplacement lava-flow deformation1199
were reported at Etna, where subsidence of tens of cm per year was measured1200
on lava that had erupted several years previously. Because the deformation1201
extended beyond the margins of these flows, the subsidence was attributed1202
to compaction of deposits and relaxation of the substrate due to loading1203
(Briole et al., 1997). A subsequent analysis by Stevens et al. (2001) on1204
a larger lava flow field at Etna corroborated the earlier results. Since the1205
initial report from Etna, lava flow subsidence, generally attributed to thermal1206
contraction, mechanical compaction, and loading over various timescales,1207
has been documented by InSAR at volcanoes around the world, including1208
Nyamulagira, Democratic Republic of the Congo (Wauthier et al., 2013);1209
Hekla, Iceland (Grapenthin et al., 2010; Ofeigsson et al., 2011); and Okmok,1210
Alaska (Lu et al., 2005a) (for a more complete list see Ebmeier et al. (2012)).1211
The subsidence rates measured by InSAR range from a few mm/yr to tens of1212
cm/year, the larger rate being observed in the months following the lava flow1213
emplacement. Indeed, even the now-solidified lava lakes of Alae, Makaopuhi,1214
and Kılauea Iki on Kılauea Volcano – sites of some of the original work on1215
post-eruptive deformation of lava – continue to deform decades after their1216
emplacement (see Figure 18).1217
Post-emplacement subsidence of pyroclastic flows and lahars has also1218
been documented by InSAR, for instance, at Mount St. Helens, Washing-1219
ton, and at Augustine and Redoubt volcanoes, Alaska. The 18 May 19801220
debris avalanche at Mount St. Helens emplaced approximately 2.5 km3 of1221
cold volcanic edifice, hot cryptodome, water, and ice in the upper part of the1222
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North Fork Toutle river valley, with a maximum deposit thickness of ∼200 m1223
(Voight, 1981; Glicken, 1996). InSAR data spanning periods beginning more1224
than 12 years after the volcano collapsed revealed three distinct patches of1225
subsidence in the deposit (see Figure 19). Poland and Lu (2008)) attributed1226
the subsidence to a combination of substrate compaction, deposit consoli-1227
dation, and melting of buried ice – thermal contraction was not considered1228
likely because the emplacement temperature of the deposit was probably not1229
sufficiently high to be causing over 2 cm/yr of subsidence after 20 years, and1230
the subsidence did not correspond to the thickest areas of the avalanche.1231
Subsidence of up to 20 cm/yr in patches of lahar deposits associated with1232
the 2009 Redoubt eruption is also thought to be caused by compaction due1233
to melting of buried snow and ice (McAlpin and Meyer, 2013). In contrast,1234
pyroclastic flow deposits from the 1986 eruption of Augustine volcano were1235
found to be subsiding more than 13 years later at a roughly steady rate of up1236
to ∼3 cm/yr, with maximum subsidence corresponding to maximum deposit1237
thickness. Masterlark et al. (2006) modeled the subsidence as due entirely1238
to thermoelastic contraction of an initially hot deposit, and, using a finite1239
element analysis, they were able to constrain the maximum emplacement1240
temperature of the deposit, as well as the deposit volume and thickness1241
distribution to a higher degree of accuracy than possible from combining1242
poor-resolution pre- and post-eruption DEMs. For a more complete list of1243
post-depositional processes affecting ignimbrites, see Whelley et al. (2012).1244
The recognition of post-emplacement subsidence of lava, lahars, and py-1245
roclastic flows in InSAR data provides both a warning and an opportunity for1246
volcanological studies. Scientists must be cautious when employing InSAR or1247
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other deformation data to interpret volcanic activity, as displacements of re-1248
cent surficial deposits may impact the manifestation of subsurface magmatic1249
activity (Stevens et al., 2001; Lu et al., 2005a). As an example, subsidence in1250
the north part of Kılauea’s summit caldera, possibly due to lava-flow cooling1251
and compaction, distorts deformation due to magma accumulation beneath1252
the surface (see Figure 18) and impacts the results of modeling of subsur-1253
face pressure change. To avoid bias in models of deep magma accumulation1254
at Hekla, Ofeigsson et al. (2011) masked areas of subsidence due to loading1255
and thermal contraction of recent lava flows near the summit of the volcano.1256
While a nuisance when modeling subsurface magmatic sources, deposit sub-1257
sidence can reveal important insights into the thermal, mechanical, and rhe-1258
ological behavior of volcanic materials. For example, deformation of lava and1259
pyroclastic flows can be used to estimate their thickness and emplacement1260
temperature, as well as the mechanical properties of the substrate (Master-1261
lark et al., 2006; Grapenthin et al., 2010) – parameters that might not be1262
discernible by other means but are important to geological and geochemical1263
investigations of eruptive activity.1264
4.3. Main InSAR limitations for deformation measurements1265
The applicability of SAR interferometry for measuring surface displace-1266
ments is not equal everywhere on Earth. Vegetated areas are usually prone1267
to temporal decorrelation (Zebker and Villasenor, 1992), which makes the1268
use of InSAR difficult in tropical zones, unless using L-band data. Other1269
phenomena, such as ice or snow cover and ash deposits, also lead to tem-1270
poral decorrelation, reducing the potential of InSAR (figure 21a). Where1271
scattering characteristics of the ground do remain stable, the geometry of1272
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acquisition can affect the success of InSAR, with reduced spatial resolution1273
on slopes facing the sensor (Figure 21b) and the potential for slopes facing1274
away to be in shadow (Figure 1). In addition, the approximately polar orbits1275
of SAR satellites mean that InSAR measurements are mostly sensitive to1276
vertical and east-west displacements., and not to north-south movement.1277
Another strong limitation of InSAR results from the variation in atmo-1278
spheric conditions between acquisitions, inducing changes in the phase delay1279
that may be misinterpreted as surface displacement (Zebker et al., 1997;1280
Hanssen, 2001). Ionospheric effects can be an error source particularly in L-1281
band data but can be mitigated in a number of ways (Meyer, 2011). However1282
the main atmospheric noise contribution in SAR data comes from the tro-1283
posphere. Tropospheric properties vary on two characteristic lateral scales:1284
short (few km), induced by turbulent troposphere dynamics, and long (10s1285
of km), due to variations in temperature and humidity profiles within the1286
atmosphere.1287
The turbulent variation still remains difficult to model but can be re-1288
duced by temporal filtering (e.g., Schmidt and Burgmann, 2003; Hooper1289
et al., 2012a) (see section 2.5). The long-scale variation induces not only1290
long-wavelength artifacts, but also interferometric fringes that are strongly1291
correlated with topography, as phase delay depends on how far through the1292
troposphere the signal has traveled (figure 21c,d). Temporal filtering meth-1293
ods are not always sufficient to reduce the topographically correlated delay1294
because SAR data sets typically do not evenly sample seasonal atmospheric1295
fluctuations, resulting in biased estimates (Doin et al., 2009). Furthermore,1296
temporal filtering methods are never able to completely separate tropospheric1297
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delay from deformation that is not steady-state.1298
Topographically correlated tropospheric artifacts can be partially cor-1299
rected using the information contained within the SAR data, based on the1300
correlation between phase and elevation in non-deforming areas (e.g., Cava-1301
lie et al., 2007; Remy et al., 2003; Lin et al., 2010; Shirzaei and Burgmann,1302
2012). In addition, the long-wavelength artifacts can be estimated over a1303
whole image by considering the variation of this correlation (Bekaert et al.,1304
in revision). Complementary data sets, such as dense GNSS or meteorological1305
measurements acquired at the same time as the SAR images, or meteorologi-1306
cal models, provide an alternative method for estimating the topographically1307
correlated and long-wavelength tropospheric artifacts (e.g., Webley et al.,1308
2002; Foster et al., 2006; Wadge et al., 2006; Doin et al., 2009; Jolivet et al.,1309
2011; Gong et al., 2011). As emphasized in section 4.2.2, the poor tempo-1310
ral sampling of SAR data has been an important limiting factor in tracking1311
magma migration. Transient deformation with a duration of hours, as some-1312
times occurs around the craters of andesitic volcanoes (Voight et al., 1998),1313
is also far below the temporal aliasing threshold of SAR data. The relatively1314
short period of satellite observations – twenty years for C-band data and even1315
less for L-band data that are more suitable for tropical areas – also limits1316
how representative probabilistic studies based on SAR data can be (Biggs1317
et al., 2014).1318
All of the difficulties described above are compounded for andesitic stra-1319
tovolcanoes. These edifices are often characterized by steep slopes, which1320
are highly vegetated due to long repose periods between eruptions, can be1321
partially covered by ice or snow, and undergo short-duration deformation1322
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related to shallow conduit and dome activity. They are thus particularly1323
challenging to study with InSAR (Pinel et al., 2011).1324
5. Key constraints on volcanic edifice growth and stability1325
In the preceding sections we have reviewed the numerous applications1326
of SAR data, from mapping eruptive deposits (section 3) to constraining1327
various styles and mechanisms of deformation (section 4). While all of these1328
applications are of tremendous value individually, the real power of SAR lies1329
in the ability to integrate its uses to better constrain large-scale dynamic1330
processes related to active volcanism. An example of this capability is the1331
investigation of volcano growth and stability.1332
Volcano growth can occur both endogenously, via intrusions and cumulate1333
formation beneath the surface, and exogenously, through the addition of lava1334
and pyroclastic material to the surface. Assessing the dominant mode of1335
volcano growth at any given time is far from an academic exercise – in lava1336
domes, for example, endogenous growth may lead to over steepening, which1337
promotes dome collapse (Kaneko et al., 2002). Endogenous growth of an1338
entire volcanic edifice can similarly lead to flank instability, at both silicic1339
stratovolcanoes (Lipman et al., 1981; Moore and Albee, 1981) and basaltic1340
shield volcanoes (Clague and Denlinger, 1994). Changes over time in the1341
proportion of extruded to intruded material provide insights into the behavior1342
of a volcano, indicating, for example, that the propensity to erupt might1343
have decreased after a strong earthquake created more open space within the1344
magma plumbing system (Dzurisin et al., 1984). General quantification of1345
intruded plus erupted material can also help to constrain the magma supply1346
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to the volcano – one of the primary controls on eruptive activity (Poland1347
et al., 2012) – as well as the ability of the volcanic edifice itself to modulate1348
magma storage (Got et al., 2013).1349
Exogenous volcano growth can be measured directly by a variety of tech-1350
niques, from geologic mapping of the thickness and distribution of lava or1351
pyroclastic material to remotely sensed changes in topography. Endogenous1352
growth, on the other hand, necessarily relies upon inferences from monitoring1353
data, including models of surface deformation (Biggs et al., 2010), thermal1354
emissions (Francis et al., 1993; Kaneko et al., 2002), gravity data (Johnson1355
et al., 2010; Flinders et al., 2010), and gas emissions (Allard, 1997). Defor-1356
mation monitoring with InSAR is uniquely suited to constrain edifice growth1357
caused by subsurface magma accumulation, as detailed in section 4.2.1. In-1358
SAR has detected large flank displacements associated with magmatic intru-1359
sions on several volcanoes, among them Etna (Solaro et al., 2010; Bonforte1360
et al., 2011; Ruch et al., 2012) and Piton de la Fournaise (Clarke et al., 2013).1361
Combining models of subsurface volume change from InSAR with measure-1362
ments of erupted volume can constrain the intrusive and extrusive growth1363
of an edifice. For example, Pritchard and Simons (2004), in an arc-wide1364
study over the Central Andes, constrained the intrusive growth rate to be1365
one to ten times larger than the extrusive rate. Biggs et al. (2010) found that1366
the volume of intrusion determined from InSAR data beneath Tungarahua1367
volcano, Ecuador, was roughly equivalent to the volume of extrusion over1368
the same time span, and the combined intrusive-extrusive volume was about1369
the same as the magma flux of the volcano over the past 2300 years. Sim-1370
ilarly, Fukushima et al. (2010) used InSAR data spanning activity of Piton1371
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de la Fournaise volcano between 1998 and 2000 to show that only 17% of1372
the magma supplied to the volcano during that time was stored, with the1373
rest erupting during 5 discrete episodes of activity. Such estimates are nec-1374
essarily rough, since they do not account for magma compressibility in their1375
volume-change modeling (Rivalta and Segall, 2008); nevertheless, they pro-1376
vide important bounds on processes that would otherwise be unconstrained.1377
Understanding whether volcano growth is dominantly intrusive or extrusive1378
can even help to explain edifice morphology. For example, models to explain1379
the shapes of Galapagos shield volcanoes, which have steep middle slopes1380
but gentle upper and lower slopes, included both construction by effusion1381
(Simkin, 1981) and magmatic tumescence (Cullen et al., 1987). Only when1382
InSAR data that spanned multiple episodes of inflation, deflation, and erup-1383
tion became available could tumescence could be ruled out as a mechanism1384
(Bagnardi et al., 2013).1385
As detailed previously, SAR data can also be used to calculate the volume1386
of surfacial deposits through amplitude imagery (Wadge et al., 2011), phase1387
differences indicating topographic change over time (Ebmeier et al., 2012),1388
and direct determinations of topography and calculations of topographic dif-1389
ferences over time (Poland, 2014). Thus SAR can simultaneously quantify1390
the volumes of both intrusion and extrusion over a given time period. Kılauea1391
provides an excellent example of both the application of this capability and1392
the potential implications for evaluating the magma budget of a volcano, as1393
demonstrated by Poland (2014). During September 2011 through June 2013,1394
TanDEM-X data acquired over Kılauea were used to construct a time series of1395
DEMs, from which it was possible to calculate the erupted volume over that1396
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span. The derived subaerial effusive volume over the ∼ 2 years was about1397
123 ∗ 106 m3 (Figure 11), but this volume is a minimum because some lava1398
entered the ocean. During periods of no ocean entry, the TanDEM-X-derived1399
lava discharge rate represents the entirety of Kılauea’s effusive output. SAR1400
data covering the same time period can be used to assess volumes of magma1401
storage as well. Deformation of Kılauea during the same time spanned by the1402
TanDEM-X time series indicates relatively minor deformation (Figure 22).1403
The pattern of displacements across the volcano is complex owing to interac-1404
tions among numerous processes, including subsidence of the Southwest Rift1405
Zone, uplift around the flanks of the East Rift Zone, subsidence of cooling1406
lava, faulting, and summit inflation due to magma accumulation. Although1407
obscured by long-term subsidence of the northern part of the caldera due to1408
lava flow cooling, summit deformation is otherwise consistent with inflation1409
of a magma reservoir at about 1.5 km beneath the caldera center (Lund-1410
gren et al., 2013). The modeled volume increase in this reservoir, however,1411
is probably less than 1 million m3 based on analogy with past inflation and1412
deflation of this source (Baker and Amelung, 2012; Lundgren et al., 2013).1413
Even accounting for magma compressibility, which could increase the volume1414
of stored magma by several orders, summit volume change is a very small1415
percentage (< 5%) of the effusive output. Magma may also be stored within1416
the East Rift Zone-especially in an area of uplift that is a result of transient1417
deformation following a dike intrusion and fissure eruption in March 2011.1418
This uplift, however, is not sufficient to account for the large discrepancy1419
between the erupted and intruded volume – even if the intruded volume is1420
increased by several times to account for magma compressibility. It there-1421
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fore seems that most magma that was fed to Kılauea during mid-2011 to1422
mid-2013 was erupted.1423
By combining the erupted and intruded volumes, the total volume of1424
magma supply to the volcano during this period can be inferred. Since the1425
start of quasi-continuous eruptive activity in 1983, magma supply to Kılauea1426
has generally fallen within the range of 4−8 m3/s (Poland et al., 2012). Since1427
most magma supplied to Kılauea also erupted during mid-2011 to mid-2013,1428
and the eruption rate, determined from TanDEM-X-derived DEMs, is about1429
2 m3/s (Figure 11b), it seems probable that the magma supply to Kılauea1430
during this period was lower than at other times during the 1983-present1431
eruption. This valuable constraint on magma supply, which is consistent with1432
sluggish lava flow activity on Kılauea’s East Rift Zone, would not otherwise1433
be measurable, especially since changes in the pattern of degassing since1434
the start of summit eruptive activity in 2008 have introduced substantial1435
uncertainty in gas-based methods for measuring magma supply and lava1436
discharge rate (Poland, 2014). In addition to long-term supply (measured1437
over years), frequent repeats by SAR satellites over Kılauea provide evidence1438
for short-term changes in the rates of intrusion and effusion (measured over1439
weeks to months). In mid-2012, the effusion rate plummeted at the same time1440
as summit deformation, as indicated by InSAR time series and continuous1441
GPS data plateaued (Figure 11b,c). Because no lava was entering the ocean1442
at this time, the decreased lava discharge and lack of inflation may indicate1443
a short-term (2-month-long) decrease in magma supply to the volcano.1444
The Kılauea case cited above provides an illustrative example of the in-1445
sights that are possible thanks to the high temporal and spatial resolution of1446
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SAR data, coupled with the capability of those data to provide information1447
on not only subsurface magma accumulation and withdrawal, but also vol-1448
ume change at the surface. No other data source currently in use offers such a1449
diversity of potential products, which range from tracking short-term, small-1450
scale changes in intrusive and eruptive activity to providing information that1451
constrains rates of magma supply to a given volcano.1452
6. Discussion1453
6.1. Looking back: advances made possible from SAR studies of volcanoes1454
For the past 20 years, satellite SAR imagery has provided repeat views1455
of Earth’s surface without regard to weather conditions or time of day, en-1456
abling detection and quantification of changes on the ground. In particular,1457
quantification of surface displacements on the order of a few millimeters is1458
possible over areas of thousands of square kilometers, and surface-change1459
data can be easily obtained from remote areas where ground-based observa-1460
tions are not practical. Volcanology and other Earth science disciplines have1461
derived outstanding benefits from this remote sensing technique by using the1462
imagery to address critical questions about how volcanoes work.1463
These capabilities provide more than just an additional dataset with1464
which to study a particular volcano. Instead, they have led to fundamen-1465
tal new insights into our understanding of volcano behavior. For example,1466
studies of the Galapagos archipelago were largely geological and petrologi-1467
cal in nature until InSAR revealed the dynamic nature of those volcanoes –1468
some of the fastest deforming in the world (Amelung et al., 2000). InSAR1469
results also demonstrated the mechanism for the pattern of eruptive vents1470
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on western Galapagos volcanoes, where fissures are circumferential near the1471
summit calderas and radial on the flanks. Decades of geological field work1472
and modeling suggested a range of possibilities for that geometry, ranging1473
from stress reorientations near summit calderas to magma chamber shapes,1474
but it was pre- and co-eruptive interferograms that revealed that the under-1475
lying assumption of such studies – that all fissures were underlain by vertical1476
dikes – was incorrect, and that sill emplacement and rotation was the source1477
of the fissure pattern (Bagnardi et al., 2013). Without InSAR, it would1478
be doubtful if the range of deformation behavior of all Galapagos volcanoes1479
would be known, and the structure and growth patterns of the volcanoes1480
would certainly be unconstrained.1481
SAR data have also provided information on volcanoes that are deform-1482
ing and should be considered ”active” with respect to potential future erup-1483
tive activity. Such studies have come in the form of focused investigations1484
on individual volcanoes, like South Sister (Wicks et al., 2002), as well as1485
regional investigations of volcanic arcs (e.g., Pritchard and Simons, 2002,1486
2004; Chaussard and Amelung, 2012; Lu and Dzurisin, 2014). InSAR thus1487
demonstrates the potential to detect volcanoes under which magma is ac-1488
cumulating and that may become active within months to years (Dzurisin,1489
2003) – a capability borne out by the statistical correlation between defor-1490
mation and eruption (Biggs et al., 2014). Just as importantly, InSAR results1491
have demonstrated that some eruptions are not preceded, nor accompanied1492
by, measureable deformation (e.g., Zebker et al., 2000; Pritchard and Simons,1493
2002; Lu and Masterlark, 2003; Moran et al., 2006; Chaussard et al., 2013;1494
Ebmeier et al., 2013b). In some cases, frequently active volcanoes, like Mer-1495
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api (Chaussard et al., 2013) and Shishaldin, Alaska (Moran et al., 2006)1496
have no deformation detected by InSAR despite multiple eruptions. These1497
observations have led to several hypotheses, including that magma ascends1498
so quickly that it effectively evades detection (since volumes of intrusion and1499
withdrawal would roughly balance one another, meaning that the net volume1500
change was zero during the time spanned by a several-week interferogram),1501
and that frequently erupting volcanoes are sufficiently “open” that pressure1502
changes are not sufficient to cause surface deformation. The growing volume1503
of SAR acquisitions over such volcanoes, with the time between observations1504
moving from ∼monthly to ∼daily, will help to address this critical question.1505
Indeed, the large numbers of studies that have documented no deformation1506
before or during eruptions are a valuable first step towards understanding1507
how these volcanoes work, and how they should be monitored for future1508
changes in eruptive activity.1509
In addition to furthering research into magmatic and volcanic processes,1510
SAR studies of volcanoes have prompted advances in data processing tech-1511
niques that have had an impact far beyond the field of volcanology. An1512
example is the problem of atmospheric artifacts in interferograms, which is1513
a major limitation of InSAR (Zebker et al., 1997; Hanssen, 2001). Tropo-1514
spheric artifacts that correlate with topography are particularly problematic1515
for volcanoes, since deformation due to magmatic processes is also likely to1516
be correlated with topography. In fact, in the first study that applied satellite1517
SAR to volcano deformation (Massonnet et al., 1995), part of the signal that1518
was interpreted to reflect a magmatic process was later found to be a result1519
of atmospheric conditions (Delacourt et al., 1998). To combat this issue at1520
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volcanoes, a variety of mitigation strategies have been explored, all of which1521
have applications to other uses of InSAR (e.g., Beauducel et al., 2000; Dela-1522
court et al., 1998; Wadge et al., 2002b; Webley et al., 2002; Remy et al., 2003;1523
Foster et al., 2006; Pinel et al., 2011), and work on the problem is ongoing.1524
Volcanoes are also often characterized by low coherence due to vegetation,1525
steep topography, ash deposition or lava emplacement, rapid deformation,1526
and ice and snow cover; new processing strategies have been developed to1527
overcome these challenges. To recover deformation associated with individ-1528
ual volcanic events, improved registration of phase data between SAR im-1529
ages (Yun et al., 2007) and pixel-offset tracking methods (Casu et al., 2011)1530
have yielded excellent results. Likewise, the non-linear nature of volcano1531
deformation over time, coupled with poor coherence, has been addressed by1532
time-series approaches, like Persistent Scatterer InSAR and the small base-1533
line approach (Hooper et al., 2004; Hooper, 2008). Modeling strategies have1534
also advanced in response to volcano deformation data provided by InSAR.1535
Most inverse models of displacement data from volcanoes are kinematic, but1536
newly developed dynamic inversions are capable of resolving the pressure1537
distribution within a magmatic body, thereby constraining the force respon-1538
sible for the measured displacements (e.g., Fukushima et al., 2005, 2010; Yun1539
et al., 2006; Hooper et al., 2011). Modeling methods that allow for increased1540
source complexity are also a direct consequence of high-spatial-resolution In-1541
SAR measurements of volcano deformation (Masterlark and Lu, 2004) and1542
the first attempt to use time-dependent optimization techniques on InSAR1543
data was at a volcanic field (Shirzaei and Walter, 2010).1544
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6.2. Looking forward: better understanding of volcanoes and forecasts of1545
eruptions1546
Given that the SAR community is on the cusp of a ”golden age”, with1547
the recent or imminent arrival of new satellites and capabilities, how will1548
volcano SAR evolve over the next 20 years? And will these new data and1549
methods bring the community any closer to a better understanding of volcano1550
behavior and to the main expectation of society with regards to volcanology1551
– forecasting (or, dare we say, predicting) eruptive activity?1552
The main impact of SAR studies on risk assessment has so far been its1553
ability to detect unrest at volcanoes where no manifestations of activity had1554
previously been reported (e.g., Wicks et al., 2002; Pritchard and Simons,1555
2002). However the role of SAR data during an eruptive crisis has remained1556
subdued. Thus far, volcano SAR studies have, in general, been used to char-1557
acterize processes after they have occurred, which is valuable for advancing1558
understanding of volcanic behavior but is less useful for hazard mitigation1559
during volcanic crises. A noteworthy exception is the aforementioned case1560
of the 2010 Merapi eruption, in which timely SAR data, especially ampli-1561
tude imagery, provided much of the basis for maintaining evacuation zones1562
and preventing loss of life during explosive eruptions of the volcano (Pallis-1563
ter et al., 2013). In Iceland too, SAR data were used in hazard assessment1564
during the 2010 eruptions of Eyjafjallajkull (Sigmundsson et al., 2010). This1565
success was made possible by rapid availability of imagery to scientists, and1566
it provides an example of the potential value of SAR to volcano monitoring1567
and risk management. Efforts are currently in progress to integrate SAR data1568
into operational volcano monitoring systems (Meyer et al., 2014), and new1569
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satellite missions will address this need – for example, the capability of near-1570
real-time data transmission from Sentinel-1 – so both amplitude imagery and1571
interferometric SAR should play a greater role in timely volcano monitoring1572
in the future. Additional satellites and the shorter repeat times of those mis-1573
sions will also help to address the need for better temporal resolution, since1574
many volcanic processes – like dike intrusions and dome growth – occur over1575
timescales of hours to days. In this new age for SAR, the community must1576
also take care to develop new processing and archiving strategies to deal1577
with the ever-increasing volume of data and to integrate SAR information1578
with ground-based observations. Only with such support systems can the1579
potential for InSAR as a near-real-time monitoring system be realized.1580
Another step that must be taken is the integration of the continuous flux1581
of SAR data with other observations into dynamic physics-based models of1582
volcanic processes. Such models have demonstrated outstanding potential1583
for investigating parameters like magma compressibility, magma reservoir1584
volume, and melt volatile content – factors that cannot be determined from1585
kinematic inversions (Anderson and Segall, 2011, 2013). Physics-based mod-1586
els also have the potential to transform our ability to forecast future activity1587
by coupling deformation, effusion rate, gas emissions, and other monitor-1588
ing data with a physically realistic model of a volcano (i.e., a model that1589
follows physics-based principles, like conservation of mass and momentum).1590
The great advantage of such an approach is that the probabilities of a spe-1591
cific outcome can not only be calculated, but also updated as new data are1592
acquired (Segall, 2013). Basic physics-based models of the evolution of an1593
eruption rely most heavily upon surface deformation and effusive volume –1594
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two variables that can be constrained by SAR measurements. Those parame-1595
ters, derived from frequent SAR acquisitions by ground-based, airborne, and1596
satellite sensors, can therefore be fed directly into models that provide prob-1597
abilistic forecasts of, for example, the potential duration of eruptive activity.1598
As physics-based models of volcanoes continue to develop, broader forecasts1599
of parameters, like eruption onset and potential volume, will become pos-1600
sible. While these models will also need to incorporate other geological,1601
geophysical, and geochemical observations where they are available, remote1602
measurements from SAR will provide the foundation upon which to build.1603
At present, the knowledge needed to develop physics-based models is avail-1604
able from only a few well-studied volcanoes, like Kılauea, Etna, Mount St.1605
Helens, and Soufriere Hills. We expect more volcanoes to join these ranks,1606
however, as the record of space-based observations grows, even for remote1607
volcanoes that are not well monitored by terrestrial methods. Within a few1608
years, it is not unrealistic to think that volcano forecasts will be based not1609
on past experience, but instead on quantitative models that use near-real-1610
time SAR and other monitoring data, to dynamically update probabilistic1611
forecasts of activity.1612
In 20 years, SAR has gone from a specialized research tool that required1613
significant expertise and computing power and was available to only a few1614
researchers, to a broadly accessible technique that can be exploited using a1615
variety of software packages and that is close to becoming a vital volcano-1616
monitoring strategy. With the launch of new satellites, development of ad-1617
ditional ground-based and airborne systems, greater availability of data, and1618
continued refinement of data manipulation methods, we expect that barriers1619
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to the use of SAR will continue to crumble, and that the technology will1620
evolve toward its logical place as a globally accessible volcano research and1621
monitoring tool.1622
7. Conclusion1623
Volcanology has derived outstanding benefits from SAR imagery by using1624
this remote sensing technique to address critical questions about how volca-1625
noes work, such as the timing of magma transfer, mechanisms of magma1626
ascent, construction of volcanic edifices, and volcano-volcano and volcano-1627
tectonic interactions. SAR amplitude data have documented eruptive de-1628
posits and surface change and have the advantage over optical data of being1629
able to operate at all times of day and in all weather conditions. Interfero-1630
metric data have been used to map deposits (from coherence), topography,1631
and deformation. Surface displacement fields derived from SAR benefit from1632
a large spatial coverage and unrivaled spatial resolution. They have thus1633
been key to assessing the shapes and volume changes of subvolcanic magma1634
plumbing systems, the impacts of magma storage and transport on local1635
stress field and future eruptive activity, and the stability of volcanic flanks.1636
SAR data should not, however, be considered independently of other vol-1637
canological datasets, but instead be integrated with these additional sources1638
of information, including both remote and ground-based data, to fully ex-1639
ploit the benefits of all. Thus far, the primary limitations in the use of SAR1640
for volcano monitoring have been the latency in data access and difficulty1641
in data processing, as well as poor temporal sampling. With the greater1642
availability of data made possible by the launch of new satellites, coupled1643
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with recent computational and technical advances that facilitate processing1644
of large datasets, SAR imagery has become a common research and monitor-1645
ing tool across the Earth sciences. The current challenge for the volcanolog-1646
ical community is the effective use of these new SAR resources for better1647
understanding volcano behavior and addressing societys need for accurate1648
forecasts of hazardous activity. The key to overcoming this challenge rests1649
on expansion of efforts to educate volcano scientists around the world in SAR1650
utilization and interpretation, and the development of new modeling tools1651
that can more fully exploit the richness of insights provided by SAR data1652
from volcanoes.1653
8. Acknowledgments1654
We are grateful to Dan Dzurisin for his helpful comments on the manuscript1655
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Table 1: Past and present side-looking orbital SAR missions used in vol-
canology (as of June 1, 2014). Additional data from Indian and Korean SAR
missions are also potentially available. For polarization description, ”single”
is for satellites providing SAR data acquired in only one given polarization,
”dual” is for satellites able to provide data acquired with two different po-
larizations (VV+VH or HH+HV) and ”quad” is for satellites able to provide
data in the fully polarimetric mode (HH,VV,HV,VH).
Table 2: Studies of magmatic intrusions though InSAR. If the event is clas-
sified as ”with eruption”, it means that at least one eruptive event associated
with the intrusion emplacement is observed during the period of activity. For
temporal evolution description, ’No’ means that the temporal evolution of
magma intrusion emplacement has not been described based on SAR data.
List of references can be found in Supplementary Material.
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Figure 1: Imaging geometry. The lower colored line represents a 2-D slice
through a ground surface and the angled black line above represents a single
line in a SAR image acquired by a sensor located above, to the left, and flying
into the page. The black arrow shows the direction of signal propagation from
the SAR sensor and the dashed lines represent perpendicular wave fronts
separated by the spatial resolution of the sensor. The different colors indicate
how the ground surface maps to pixels in the range direction of the SAR
image. The blue slope facing the sensor is “foreshortened” in the SAR image,
whereas the red slope facing away from the sensor is lengthened, and appears
in more pixels despite both slopes having the same length on the ground. The
green section of the steeper mountain is “laid over”, appearing in the same
pixel as the ground surface well to the left, and the yellow lower section of
the mountain appears to the right of the upper section in the SAR image,
despite being located to the left of it on the ground. The steep gray slope
facing away from the sensor is not illuminated by the sensor at all – it is in
“shadow”– and does not appear in the SAR image.
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Figure 2: Cumulative line-of-sight displacement over Eyjafjallajokull vol-
cano, Iceland, from 18 June 2009 to 01 September 2010 (track 132 ascending
mode). The co-eruptive interval includes the flank eruption, during which
there was very little deformation, and the later summit eruption, during
which the volcano deflated. Background is shaded topography. The hole in
the data is due to ice and snow cover. Black dots are earthquake epicen-
ters for each epoch (Icelandic Meteorological Office). Modified from Martins
et al. (in prep).
Figure 3: Comparison of pixels selected by a PS method and a small base-
line method from ERS data acquired over Eyjafjallajokull volcano, Iceland,
modified from Hooper (2008). The volcano is undergoing inflation due to the
intrusion of a sill at 5-6 km beneath the southern flank. Left, pixels selected
by the PS method of Hooper et al. (2007) and right, pixels selected by the SB
method described in Hooper (2008). The pixels are plotted on topography in
shaded relief, with white representing the approximate area of permanent ice
cover. The location of the area analyzed is shown left inset. 27 images were
used in the analysis although only one interferogram is shown here, which
covers 27 June 1997 to 10 October 1999. Each color fringe represents 2.8 cm
of displacement in the line-of-sight.
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Figure 4: Temporal evolution of the number of peer reviewed scientific papers
based on satellite SAR data and applied to the study of volcanoes published
before 2014. We make a distinction between studies using only data provided
by ESA (ERS-1, ERS-2 and ENVISAT). A significant increase is observed
since 2010 due to the broad exploitation of L- and C-band data. The list of
scientific paper is from ISI Web Of Science. (See supplementary material for
references.) In insert, for comparison, the list of scientific papers obtained a)
with the two topics “volcanoes” and “deformation” and b) with the topics
“SAR” and (“earthquake” or “landslide” or “subsidence”) using ISI Web Of
Science.)
Figure 5: C-band (top) and L-band (bottom) interferograms spanning June
17–19, 2007, East Rift Zone intrusion and eruption at Kılauea Volcano,
Hawai‘i. Data from the L-band ALOS satellite are more coherent than the
ENVISAT C-band satellite, which is important for interpreting deformation
patterns that extend into forested regions. Deformation patterns indicate
deflation of Kılauea Caldera and widening and uplift in its middle East Rift
Zone as magma drained from beneath the summit to feed a growing dike
within the rift zone (Poland et al., 2008). Both interferograms have the same
scale for line-of-sight surface displacements–5 cm per color cycle. Satellite
flight directions and look angles from vertical are given by the arrows in the
upper left of each image.
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Figure 6: Amplitudes images (TerraSAR-X) of the summit area of Merapi
volcano during the October-November 2010 eruption. Large changes in the
dome were observed over the course of the eruption. A) Image acquired 26
October at 22:21 UTC (Descending Track 134, incidence angle: 36◦). The
first explosive event (26 October 10:00 UTC) has just removed the 2006 lava
dome, enlarging and deepening the crater. B) Image acquired 4 November
11:00 UTC (Ascending Track 96, incidence angle: 47◦) showing a large (≈
5∗106m3, (Pallister et al., 2013)) lava dome. C) Image acquired 6 November
22:21 UTC (Descending Track 134., incidence angle: 36◦). A new lava dome
has grown to a volume of approximately 1.5 ∗ 106m3 (Pallister et al., 2013)
after the total destruction of the former one by the explosion on 4 November
17:05 UTC. See Pallister et al. (2013) for a complete overview.
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Figure 7: Deposits of the 2010 eruption of Merapi volcano observed by
SAR imagery. a) Location of Merapi volcano in the central part of Java
island (the white box corresponds to the area covered in parts b and c). b)
ALOS-PALSAR amplitude-change image of Merapi’s southern flank. The
false-color composite (R: earlier image; G: later image; B: ratio of the sec-
ond image divided by the first image) is obtained using pairs of amplitude
images acquired in HH polarization before the eruption (on 16 September
2010) and after the event (on 1 February 2011). Deposit characteristics are
known from field observations and optical imagery (Charbonnier et al., 2013)
c) Coherence image obtained when forming the interferogram between two
descending ALOS/PALSAR images acquired before the main eruptive phase
(on 1 November 2010) and after (on 17 December 2010). The area covered by
deposits from the eruptive activity is characterized by a strong decorrelation
and appears dark. Figure is modified from Solikhin et al. (in revision).
Figure 8: Co- (A) and Cross-polarized (B) RADARSAT-2 amplitude images
acquired on July 7, 2010, depicting the summit, south flank, and upper East
and Southwest Rift Zones of Kılauea Volcano, Hawai‘i. Variations in surface
roughness, particularly between recent ‘a‘a and pahoehoe lava flows (like
those erupted in December 1974 from the Southwest Rift Zone and during the
1969-1974 Mauna Ulu eruption on the East Rift Zone), appear as differences
in shading (light areas indicate high backscatter and dark regions, low) and
are best distinguished in the cross-polarized image.
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Figure 9: Co- (A) and Cross-polarized (B) RADARSAT-2 amplitude images
acquired on January 23, 2014, depicting the 1983-2014 lava flow field from
the Pu‘u ‘O‘o eruption. At the time of image acquisition, the northern-most
portion of the pahoehoe flow field was the only area with active lava, which
was extending into forested areas. Co-polarized imagery is not useful for
discriminating lava from forest, but the cross-polarized data allow for easy
mapping of flow margins given the backscatter contrast between lava (dark)
and forest (light).
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Figure 10: Example application of coherence to map lava flows, modified
from Figures 10-12 from Dietterich et al. (2012). (A) Extent of a lava break-
out from an eruptive vent located ∼2 km east of Pu‘u ‘O‘o crater based
on coherence mapping from Envisat interferograms. Breakout started on
November 21, 2007, and colors indicate lava extent over the subsequent ∼110
days. Advance rate (inset; green is western branch of flow indicated by green
arrow on map, and blue is eastern branch indicated by blue arrow on map)
can be calculated based on the position of the flow front over time. Gap
in flow is due to the presence of vegetation, which, like lava, is incoherent.
Gray dashed box indicates area covered by part B. (B) Length of time from
emplacement until lava, which erupted during July-November 2007, becomes
coherent (colors) compared to flow thickness as measured on the ground
in October 2007 (gray contours). Distal extent of flow was in the forest,
and therefore the length of decorrelation is not measurable, while vent area
remained incoherent for longer than other parts of the flow because it re-
mained active long after the main flow area ceased activity in November
2007. Decorrelation time and thickness correspond strongly, indicating that
the time from emplacement that is required for a lava flow to become coher-
ent can be a good indicator of flow thickness. Inset gives thickness versus
time until coherence for the mean length of time a pixel is decorrelated in the
areas between each of the thickness contours. Error bars show the standard
deviation, red line is the best fit, and shaded region indicates the time during
which the flow was active.
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Figure 11: Four-dimensional growth of the East Rift Zone lava flow field at
Kılauea Volcano, Hawai‘i (modified from Poland (2014)). (A) Map showing
thickness of lava emplaced between August 2011 and June 2013. Colors cor-
respond to flow thickness. Map is calculated from the cumulative thicknesses
determined from 16 time-sequential TanDEM-X DEM difference maps, which
individually show topographic change during discrete time intervals within
the overall time spanned. (B) Time-averaged discharge rate of lava (left
axis = bulk; right axis = dense-rock equivalent assuming 25% vesicularity)
from Kılauea’s East Rift Zone during 2011-2013 based on a time series of
TanDEM-X DEMs. The volume of lava for each time interval was calculated
by multiplying the thickness over the area covered. Bar thickness is greater
than measurement uncertainty. Gray shaded areas indicate times when lava
entered the ocean. Discharge rates determined during these periods are min-
imum values, since lava that flows into the ocean is not accounted for by
changes in topography. (C) Deformation of Kılauea’s summit during mid-
2011 to mid-2013 measured at a GPS station near the center of the caldera
(gray line, left axis) and a nearby pixel located at the point of maximum LOS
deformation (black circles with error bars, right axis). InSAR time series is
from COSMO-SkyMed ascending data. GPS and InSAR measurement points
are given in Figure 22. Red box shows time period when the discharge rate
of lava decreased drastically, coupled with a plateau in long-term summit
inflation; these indicators suggest a short-term decrease in magma supply
rate to the volcano.
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Figure 12: Various type of deformation related to volcanic activity and ob-
served by InSAR. Deformation may be due to magma storage/emplacement
at depth either as reservoirs or intrusions. Data used for illustration are
modified from Vadon and Sigmundsson (1997); Froger et al. (2004); Lu et al.
(2010); Bonforte et al. (2011); Pinel et al. (2011).
Figure 13: Vertical and horizontal surface displacements induced by the
September 2005 Manda Hararo-Dabbahu rifting event, Afar (Ethiopia) de-
duced from a pixel-by-pixel inversion of displacements provided by InSAR
and subpixel correlations of synthetic aperture radar and SPOT images.
Courtesy of R. Grandin and modified from Grandin et al. (2009).
Figure 14: Inferred depth of deformation sources beneath volcanoes that
have been systematically observed by InSAR, as listed by Biggs et al. (2014)
(their supplementary material, tables 3b, 4b, 4c and 5). Volcanoes showing
only subsidence related to eruptive deposits are excluded.
Figure 15: Perspective view of an interferogram, which spans 1996-2000,
draped over a 30-m DEM of South Sister volcano, central Oregon. Center of
inflation is located about 5 km west of the volcano’s summit. Modified from
Wicks et al. (2002).
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Figure 16: A deep dike intruded beneath the Upptyppingar region of N.
Iceland in 2007. The orientation was not perpendicular to the direction of
least compressive stress, and slip was therefore induced across the dike. Top
panels show interferograms spanning the intrusion (left, ascending and right,
descending) with each colour fringe representing 28 mm of displacement. Also
shown are horizontal GPS velocities with 95% confidence ellipses, surface
projections of the model source patches (white rectangles) and catalogue
earthquake epicentres for the entire intrusion period (black circles). Bottom
left panel shows the surface projection of the modelled mean opening and slip
from the posterior probability distribution. The right panel shows the dike
position in profile, with the maximum posterior probability solution plotted
in red and other solutions from the probability distribution plotted in grey.
Modified from Hooper et al. (2011).
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Figure 17: Deformation of Fernandina volcano, Galapagos, associated with
its 2009 eruption, modified from Bagnardi et al. (2013). All interferograms
acquired by ENVISAT, and arrows in lower right corners indicate satellite
flight direction (small arrow) and look direction (thick arrow). (A) Typi-
cal inter-eruptive inflation pattern, with deformation mostly confined to the
summit caldera. This particular interferogram spans January 16-August 14,
2010. (B) Interferogram spanning January 31-April 10, 2009, with the end
time just ∼ 2 hours before the onset of eruption. Deformation is consistent
with intrusion of a sill from the 1-2-km-depth subcaldera magma reservoir.
(C) Interferogram spanning March 31-May 5, 2009, which covers the entirety
of Fernandina’s 2009 eruption. Surface displacements are a consequence of
subcaldera reservoir deflation, sill intrusion, and rotation of that sill towards
the vertical on the flank, where it intersected the surface and erupted lava
flows (yellow area).
Figure 18: COSMO-SkyMed ascending interferogram covering the summit
caldera and upper East Rift Zone of Kılauea Volcano and spanning 2 April
2012 to 19 March 2014. While a number of deformation processes are rep-
resented, several stand out, including line-of-sight subsidence in the north
part of the caldera, as well as at the former Alae and Makaopuhi lava lakes.
These three areas are sites of thick accumulations of lava that, decades after
their emplacement, are still cooling and compacting.
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Figure 19: Average line-of-sight displacement velocity at Mount St. Helens,
Washington, from a stack of 36 interferograms acquired by ERS-1/2 on track
156 during 1992-2001. With the exception of the debris avalanche deposit
on the north side of the volcano and extending down the North Fork Toutle
River valley, most of the area is incoherent owing to vegetation and snow
cover. Three patches of subsidence are apparent in the stack; these patches
also exist in data from other tracks and satellites, indicating that they are
not atmospheric artifacts and that they are long-term deformation sources.
Modified from Poland and Lu (2008).
Figure 20: Time series of vertical displacement at the caldera center of
Fernandina volcano, Galapagos, using data from a variety of SAR satellites.
Vertical deformation is derived by combining descending and ascending inter-
ferograms from each satellite. Note breaks in time scale (between 1992 and
1998) and vertical displacement scale (between 0.0 and 0.5 m). Throughout
the 19-year span, overall uplift is interrupted by episodes of deflation asso-
ciated with eruptive activity (Chadwick et al., 2011; Bagnardi et al., 2013)
and sill emplacement (Bagnardi and Amelung, 2012). 1995 eruption is not
indicated. Modified from Baker (2012) and Bagnardi (2014).
126
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Figure 21: Illustration of some limitations of InSAR using the example of
a typical andesitic stratovolcano, Colima Volcano, Mexico (NCV: Nevado de
Colima Volcano, CV: Colima Volcano, GC: Guzman city, CC: Colima city).
a) Mean coherence obtained from 54 descending interferograms from 2003 to
2006. Good coherence is restricted to towns or villages located at the base of
the volcano and to recent lava flows covering the upper part of the volcano;
other areas are highly vegetated. Another cause of decorrelation is due to
ash fall and unconsolidated explosive deposits in the vicinity of the volcano’s
summit. b) Areas not well imaged on SAR images due to the acquisition
geometry, are superimposed in grey on the contour lines (200 m) derived from
the SRTM Digital Elevation Model. Data are acquired by a satellite traveling
from north to south (descending track) with a look angle of about 22 degrees.
The satellite motion is shown by the black arrow and the look direction is
shown by the gray one. 3.6 % of the scene represented is not well imaged,
mostly (98 %) because of layover effects on slopes which are oriented towards
the radar beam. c) Interferogram corrected for topographic errors obtained
from an ascending track and characterize by a perpendicular baseline of 5 m
and a temporal baseline of 385 days. Each color fringe represents a phase
difference of 2π. Fringes are strongly correlated to topography as shown by
comparison to the SRTM Digital Elevation Model (d). This signal is mostly
produced by tropospheric artifacts, which correlate with topography. a) and
b) are modified from Pinel et al. (2011); c) and d) are modified from Yan
et al. (2014).
127
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Figure 22: Ascending-mode COSMO-SkyMed interferogram of Kılauea Vol-
cano overlain on a shaded relief map and spanning September 7, 2011 to
June 20, 2013 – about the same time spanned by the TanDEM-X-derived
DEM differences that constrain the subaerial eruption volume (Figure 11a).
A variety of deformation sources are interacting to form a complex pattern
of LOS displacements, including: Southwest Rift Zone subsidence; faulting
in June 2012; subsidence of lava accumulations along the East Rift Zone and
in the north part of the caldera; inflation of the summit; and LOS uplift of
the East Rift Zone flank, which appears to be a transient related to a dike
intrusion (thick red line) that fed a fissure eruption in March 2011. Black
dot gives location of GPS station from Figure 11c; white dot is location of
pixel from which Figure 11c time series is extracted.
128
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Mission
Periodof
operation
Wavelength
Orbitrepeattime
Polarization
SEASAT
Jun-O
ct1978
23.5
cm17
days
Single
ERS-1
Jul1991
toMar
2000
5.66
cm3or
35days
Single
ERS-2
Apr1995
toSep
2011
5.66
cm3or
35days
Single
JERS-1
Feb
1992
toOct
1998
23.5
cm44
days
Single
SIR
-C/X
-SAR
9to
20Apr1994
23.5,5.8an
d3cm
N/A
Quad
and30
Sep
to11
Oct
1994
RADARSAT-1
Nov
1995
toMarch
2013
5.6cm
24days
Single
SRTM
11-22Feb
2000
5.8an
d3.1cm
N/A
Single
Envisat
Mar
2002
toApr2012
5.63
cm35
days1
Dual
ALOS
Jan
2006
toApr2011
23.5
cm46
days
Quad
COSMO-SkyMed
Jun2007
topresent
3.1cm
16days
Dual
(con
stellation
ofDec
2007
topresent
3.1cm
16days
Dual
4satellites)
Oct
2008
topresent
3.1cm
16days
Dual
Nov
2010
topresent
3.1cm
16days
Dual
TerraSAR-X
Jun2007
topresent
3.1cm
11days
Dual
Tan
DEM-X
Jun2010
topresent
3.1cm
11days
Dual
RADARSAT-2
Dec
2007
topresent
5.6cm
24days
Quad
Sentinel-1
2014
topresent
5.6cm
12days2
Dual
ALOS-2
2014
topresent
23.5
cm14
days
Quad
Tab
le1:
Pastan
dpresentside-look
ingorbital
SAR
mission
sused
involcan
ology(asof
June1,2014).
Additionaldata
from
Indianan
dKoreanSAR
mission
sarealso
potentially
available.For
polarizationdescription,”single”is
forsatellites
providingSAR
dataacquired
inon
lyon
egiven
polarization,”d
ual”is
forsatellites
able
toprovidedata
acquired
withtw
odifferentpolarizations(V
V+VH
orHH+HV)an
d”q
uad
”is
forsatellites
able
toprovidedata
inthefullypolarimetricmode
(HH,V
V,H
V,V
H).
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Typeof
event
Location
Date
Duration
Tem
poral
References
evolution
Riftingevents
Witheruption
Gelai
2007
2mon
ths
Yes
Baeret
al.(2008);Biggset
al.(2009)
Withnoeruption
HarratLunay
yir
2009
4mon
ths
Yes
BaerandHamiel(2010)
Witheruption
Man
daHararo-Dab
bah
u2005
3weeks
No
Wrightet
al.(2006),
(Mainevent)
Grandin
etal.(2009)
Witheruption
sMan
daHararo-Dab
bah
u2005-2009
5years
Yes
Grandin
etal.(2010a,b)
(Allriftingepisode)
Ham
linget
al.(2010)
Withnoeruption
Upptyppingar
2007-2008
14mon
ths
No
Hoop
eret
al.(2011)
Volcanoes
inarift
zone
With8eruption
sNyam
uragira
1996-2010
4-60
days
No
Toom
bsandWadge(2012);Wauthieret
al.(2013)
Witheruption
Nyiragon
go2002
12-48hou
rsNo
Wau
thieret
al.(2012)
Other
Volcanoes
Witheruption
Chaiten
2008
few
hou
rsNo
Wickset
al.(2011)
Witheruption
ElHierro
2011-2012
8mon
ths
Yes
Gon
zalezet
al.(2013)
Witheruption
Etna
2001
23days
No
LundgrenandRosen(2003)
Withnoeruption
Eyjafjallajokull
1994
1mon
thNo
PedersenandSigmundsson(2004)
Withnoeruption
Eyjafjallajokull
1999
9mon
ths
Yes
PedersenandSigmundsson(2006)
Witheruption
Eyjafjallajokull
2010
3mon
ths
Yes
Sigmundssonet
al.(2010)
Witheruption
Fernan
dina
1995
4mon
ths
No
Jon
ssonet
al.(1999);Amelunget
al.(2000)
Witheruption
Fernan
dina
2005
16days
No
Chad
wicket
al.(2011);BagnardiandAmelung(2012)
Withnoeruption
Fernan
dina
2006
1day
No
BagnardiandAmelung(2012)
Withnoeruption
Fernan
dina
2007
3days
No
BagnardiandAmelung(2012)
Witheruption
Fernan
dina
2009
18days
Yes
Bagnardiet
al.(2013)
Witheruption
Kilau
ea2007
2days
No
Mon
tgomery-B
rownet
al.(2010)
Witheruption
Kilau
ea2011
4days
Yes
Lundgrenet
al.(2013)
Witheruption
Kizim
en2009-2010
>1yr
Yes
Jiet
al.(2013)
Withnoeruption
Mau
naLoa
2002-2005
3.5days
No
Amelunget
al.(2007)
Witheruption
Piton
dela
Fou
rnaise
1998
3mon
ths
No
Sigmundssonet
al.(1999);Fukushim
aet
al.(2010)
With4eruption
sPiton
dela
Fou
rnaise
1999-2000
12days-1mon
thNo
Fukushim
aet
al.(2005,2010)
Witheruption
Piton
dela
Fou
rnaise
2003
4days
No
Frogeret
al.(2004)
Witheruption
Tungu
rahua
2008
1mon
thNo
Biggs
etal.(2010)
Withnoeruption
Yellowston
e1995-2000
few
years
No
Wickset
al.(2006)
Table
2:Studiesofmagmatic
intrusionsthoughInSAR.Iftheeventisclassifiedas”witheruption”,itmeansthatatleastoneeruptiveeventassociated
withtheintrusionemplacementis
observedduringtheperiodofactivity.Fortemporalevolutiondescription,’N
o’meansthatthetemporalevolution
ofmagmaintrusionemplacementhasnotbeendescribedbasedonSAR
data.Listofreferencescanbefoundin
SupplementaryMaterial.
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