52 COMPUTING IN SCIENCE & ENGINEERING

STUDYING THE EARTH WITHINTERFEROMETRIC RADAR

High-speed and large-volume computational capabilities have affected many branches ofscientific research. Interferometric synthetic aperture radar (InSAR) and its spatially dense,accurate deformation measurements have advanced studies of the Earth’s crust. The mostimportant contributions are related to seismic and volcanic processes and the mapping ofglacier and ice-sheet motions in the environmentally-sensitive and diagnostic polar regions.

E A R T H S Y S T E MS C I E N C E

The destructive power of earthquakescan be enormous. Fortunately, inmuch of the US, strict buildingcodes and luck in the timing of

many recent events have limited the amountdamage in terms of loss of life. Even so, esti-mates of property destruction from the 1994Northridge, California, earthquake are in thetens of billions of dollars. In Japan, where theemergency response infrastructure is also com-prehensive, a similar earthquake in Kobe re-sulted not only in over $100 billion of damagebut also in the loss of 6,000 lives. The recentTurkey earthquakes have led to a death toll thatmight exceed 40,000 lives.

Given such motivations, it is not surprisingthat geologists and geophysicists devote mucheffort to developing a better understanding ofseismic phenomena. Much of our currentknowledge of earthquakes has been derived froma sensitive network of seismometers that can

measure not only the large earthquakes we allfeel but also the thousands of smaller events thatoccur every day around the world. Combiningthese observations with surveying techniquesthat measure crustal deformation resulting fromseismic movement in a few widely spaced loca-tions has led to existing models of the variousprocesses that lead up to earthquakes.

Studying Earth crustal deformation

Forecasting earthquakes is currently beyondour capabilities. Seismic techniques only detectthe waves propagating through the crust after adislocation in the subsurface rock occurs—thatis, we have little warning of an event until theearthquake happens. It takes a break in the rockto initiate a measurable signal. Clearly, if wecould record deformations building up in thecrust before any catastrophic rock failure, wecould not only better describe the earthquake’sphysics but would be closer to anticipating whensuch a failure might be likely to occur.

One new technique, interferometric syntheticaperture radar (InSAR), is beginning to providemeasurements with enough sensitivity, compre-hensive coverage, and spatial density to allow us

HOWARD A. ZEBKER

Stanford University

1521-9615/00/$10.00 © 2000 IEEE

MAY/JUNE 2000 53

our first peek at the details of crustal deforma-tions similar to those we expect from interseis-mic motions. Carried 800 km or more above theEarth on specialized satellites, interferometricradar instruments produce images of surfacemovements with better than one centimeter peryear sensitivity over swaths 100 km or more inwidth and at a resolution on the ground of 10 mor better (see Figure 1). Researchers are apply-ing these capabilities not only to earthquakes butalso to other areas of deformation research, suchas volcano studies, where similar motions mightindicate imminent eruptions. Environmentalscience has already been advanced by more ac-curate and complete measurement of glacierflows in the polar regions, contributing to stud-ies of Earth’s changing climate such as globalwarming. We can better characterize surfacesubsidence phenomena, such as those resultingfrom groundwater withdrawal and oil produc-tion, using more precise observations, which willcontribute to superior resource management.

If we are to use new measurement approachesto study these phenomena, we need to charac-terize the driving forces and understand how theresulting surface deformations are distributed.The largest forces derive from tectonics, themovement of sections of the Earth’s crust. Thecrust is divided into about a dozen large platesand a score of smaller ones that essentially floaton the denser mantle below. Although re-searchers poorly understand the driving mech-anism, the plates are in constant motion andcontinually bump into each other. Over geologictime, collisions between plates are responsiblefor building great mountains. For example, acollision of the Indian plate with Asia producedthe Himalayas. In other locations, two platesmight move away from each other, causing seafloor spreading such as along the center of theAtlantic Ocean. Here, new crustal material fromthe deeper mantle is first exposed to the surfacein continuous volcanic eruptions.

In still other cases, such as in California wherethe Pacific plate meets the North Americanplate, the motion is predominantly sideways asone plate slips past the other, creating a trans-form fault. The well-known San Andreas faultrepresents the border between these two plates,ranging from the Salton Sea, near the Mexicanborder in the south, to near Cape Mendocino inthe north. Measurements of the plates’ relativemotion reveal that the North American plate isslipping southward relative to the Pacific plateat a rate of approximately 5 cm per year, of which

about 3.5 cm per year is concentrated alongmuch of the San Andreas fault zone.

We also know that on certain portions of theSan Andreas system the surface expressions of thefault creep past each other continuously at ap-proximately 2 to 3 cm per year. In other areas,principally in the southern part of the state, thetwo plates are locked together at the surface. Al-though it is the plates’ motion at depth, not onthe surface, that seems to be responsible for mostearthquakes, we can’t observe the full interplatemotion in either the creeping or locked fault’sportions. Seismic activity must periodically re-lease the consequent stress buildup within the un-derlying rocks. In California, major earthquakesof magnitude seven or more occur approximatelyevery 100 years on much of the San Andreas, re-leasing several meters of accumulated strain.

Researchers only know sketchy details abouthow these ongoing motions are distributed overthe surface. Knowing how this stress is distrib-uted as well as how it changes in different loca-tions would greatly increase our knowledge ofthis important plate boundary system structureand would perhaps yield clues as to when thesubsurface rocks are nearing slip potential.

Geophysicists have measured crustal defor-mation for more than 100 years. Traditionalmethods are descended from techniques survey-ors developed, in which the surface is dividedinto many triangular patches. Even in the lastcentury, we could measure the angles of each tri-angle very accurately and record rather smallchanges in the surface shape. However, we canobtain measurements only at a few points be-cause many spots are hard to get to, and the costof sending people out for surveys forces us to

Figure 1. Coseismic interferogramof the 1992Landers, Cali-fornia 7.3-magnitudeearthquake.The rich pat-tern of fringesshows 2.8-cmcontours ofsurface dis-placement to-ward theradar, whichwe can inter-pret to studythis event’sslip distribu-tion. (Imagecourtesy ofGilles Peltzer,JPL.)

54 COMPUTING IN SCIENCE & ENGINEERING

compromise the number of points—and hencethe area—we can survey.

These limitations aside, many experimentsused survey procedures to study crustal defor-mation. But in the 1980s, the Global PositioningSystem radically changed surveying. We can usethis set of 24 orbital space satellites, originallybuilt for military guidance and reckoning, to de-rive the location of a ground receiver anywhereon Earth to a precision of better than 1 cm. How-ever, even GPS suffers from some of the samevexing problems affecting traditional surveys. Atleast once a person must visit the site to place areceiver, and if a receiver is left behind, powermust be supplied and data retrieved. This alsolimits the areas we can study and the number ofpoints at which we can acquire measurements.So, to use GPS effectively, researchers have toknow where to look beforehand. For certain ar-eas, such as in the heavily urbanized regions ofLos Angeles and the San Francisco Bay Area, thisis less of a problem. The San Andreas, on theother hand, is over 1,000 km long, and an earth-quake could start anywhere along it and propa-gate along the fault close enough to inhabited ar-eas to cause considerable damage.

This is where the radar measurements can po-tentially revolutionize crustal-deformation in-vestigations. From their synoptic viewpoint highabove Earth, radar satellites can observe almostanywhere on the surface with ease. Because radaris immune to darkness and cloud cover, which

spurred its initial development for military re-connaissance, it is invaluable for geophysical in-vestigations. At present, some four separate civil-ian radar satellites carry out a variety of investi-gations of Earth, including crustal-deformationstudies.

The InSAR technique

A radar sensor 800 km above the Earth detectstiny changes on the ground by very accuratelymeasuring changes in the time delay, or phase,of a radar echo. Radar systems form images byilluminating a swath, or a fairly large portion ofthe surface, with a series of microwave-energypulses. Precisely analyzing of the reflected radiowaves received back at the radar, the energy isredistributed in the data processor according tothe time delay and Doppler frequency shift ofthe pulse echoes, forming a ground “image.”

Now, suppose that we acquired a second radarimage of the same ground area at a later time. Ifwe compare the radar echoes from each point onthe surface in each image, we would expect thereflected signals in the second image to be thesame as in the first image if the ground hasn’tchanged between observations. If the ground hasshifted position in the time between the images,the signal from the second image will be delayedin time proportionally to the difference in posi-tion (see Figure 2). We measure the time delay byexamining the echo signal’s phase, and we canusually determine the delay to anywhere from1/10th to 1/100th of a wavelength. Most radarsatellites operate with wavelengths of a few cen-timeters, leading to potential sensitivities of sev-eral millimeters or less. Because the two radar im-ages are differenced in phase, we call this devicefor measuring surface deformations an interfero-metric synthetic aperture radar, and its resultingpicture of phase changes an interferogram.

Neither the radar image nor the interferogramgeneration require detailed knowledge of thesurface or equipment in the field. Hence, re-searchers can avoid the limitations of the surveyor GPS approaches and can begin to think aboutmeasuring the entire Earth’s deformation. Be-cause the purview of radar satellites is global, wecan study deformation processes anywhere at lit-tle additional expense once the satellite is in op-eration. The second great advantage of the radarinterferometer system is that we can make de-formation measurements at very fine spatialspacing, creating a visual image of the deforma-tion distribution. Thus, it is not necessary to

Sensor

Distances to eachpoint unchanged

Distances to each pointchanged due to uplift

Points onfar side of

fault upliftedas a group

Figure 2. A mechanism of phase shifts for surface displacements. If apoint moves farther from the radar between observations, the timedelay for its echo and its phase in the interferogram are increased.

MAY/JUNE 2000 55

identify in advance where fine-scale distortionsmight occur, and we can readily study previouslyunsuspected regions of surface change.

Quantifying the measurements

The equations we need to calculate ground dis-placement fields from InSAR measurements arederived from geometric interpretation of the ob-served radar signal phases. Here, we assume thatthe reader has a general knowledge of radar sys-tems and processing. You can consult a generaltext on radar remote sensing, such as SyntheticAperture Radar by John Curlander and RobertMcDonough (Wiley Interscience, 1991), for ques-tions on radar system operation and processing.

Figure 3 shows basic imaging geometry. Asdiscussed earlier, suppose that we acquire twoimages of the same ground surface; differencingthese in phase forms an interferogram. First, weexamine the case where the radar is located atprecisely the same location in space, observingthe identical ground area, but some distributionof ground movement between radar observa-tions occurs. The measured phase at each pointin each of the two radar images is related directlyto the distance the signal travels. It might betaken as equal to the sum of a propagation partproportional to the round-trip distance traveledand a scattering part due to the interaction of thewave with the ground. If each resolution elementon the ground behaves the same for each obser-vation, then calculating the difference in thephases removes dependence on the scatteringmechanism and gives a quantity dependent onlyon any surface displacements between observa-tions. If we take the two path lengths ρ and ρ +δρ, with δρ as the radar line-of-sight surface dis-placement between observations, the measuredphase difference φ at wavelength λ is the inter-ferometric phase and will be

φ= −4π/λ δρ (1)

which we note is simply 2πtimes the roundtripdistance difference in wavelengths. Thus, creat-ing an image of the phase differences producesa ground displacement map in the direction ofthe radar line of sight.

It is quite difficult in practice to ensure that theradar platform returns to exactly the same posi-tion for the second observation. This is beyondour currently available navigation ability, so wemust settle for returning only approximately tothe same position. Hence, we will view the sur-

face from a position near the initial position, re-sulting in some parallax distortion in every inter-ferogram. As an added advantage, however, thisadditional displacement is invertible to recovertopography from an interferogram.

We quantify the added parallax distortion asfollows. (For the derivations here, we have as-sumed that the Earth’s surface is flat, not curved.We can derive similar expressions using a curvedsurface with some algebra and geometry, but theequations are somewhat more complicated. Inthe case of spaceborne sensors, the flat-Earth ap-proximations will usually not be accurate enoughfor geophysically-useful results.) Let the two an-tenna locations be no longer coincident, and dis-place them spatially to occur at positions A1 andA2 (see Figure 3). Because of the difference inviewing direction, even in the absence of groundmotion we observe a displacement ∆ρ that de-pends on viewing geometry and surface topog-raphy. The total displacement now consists oftwo parts: δρ for the real surface motion betweenobservations and ∆ρ for the apparent motion dueto parallax. The law of cosines permits solutionfor the total displacement δρ + ∆ρ in this case asfollows. We have

(2)

where the separation of the satellites, or baselinelength, is B, the look angle is θ, and the angle ofthe baseline with respect to horizontal at the sen-sor is α. Neglecting second-order terms yields

ρ δρ ρ ρ ρ θ α+ +( ) = + − −( )∆ 2 2 2 2B B sin

ρ

ρ + δρ

B

z

α

θ

A2

A1

A2'

ρ + δρ'

B'

α'

B ||

B

Figure 3. Imaging geometry for radar interferometry.

56 COMPUTING IN SCIENCE & ENGINEERING

. (3)

Because ρ is usually much larger than B, we canusually neglect the first term on the right handside of Equation 3, but it is not necessary to doso. The instrument’s topographic sensitivity en-ters through the dependence of the exact look an-gle θ on the altitude [z = h – ρ cos (θ)], where h isthe height of the sensor above the reference sur-face. Thus, we can use the interferometric mea-surement to determine the surface’s topography,which is often a valuable surface descriptor.

An interferogram thus contains phase variationsdependent on topography and surface defor-mation, and these effects must be separated if weare to interpret the interferograms unambiguously.

The topographic phase signature can be re-moved from an interferogram during interfero-metric processing if a preexisting digital eleva-tion model (DEM) exists (sometimes referred toas the two-pass method) or if a second interfer-ogram containing topographic signal only is ac-quired (the three- or four-pass method). DidierMassonnet and his colleagues initially developedthe two-pass method at CNES Toulouse,1 andresearchers implemented it by explicitly calcu-lating the expected interferogram assuming noground displacements. This interferogram isthen subtracted in phase from the measurements(see Equation 3), leaving a phase signature that isdominated by any real displacement effects. Inthis approach, the DEM’s precision will limit ac-curacy. Usually, DEM accuracies are satisfactoryif the posting is better than 100 m and the preci-sion is better than 10 m, although this dependssomewhat on imaging geometries.

The predicted topographic phase is subtractedfrom the interferogram pixel by pixel, leavingonly the deformation’s signature.

If the DEM does not exist in an area of inter-est or if it is of poor quality, we might instead usea three- or four-pass method Andrew Gabrieland his colleagues first described,2 and I and mycolleagues subsequently applied to the measure-ment of coseismic deformation.3 In this method,we use a third radar image, forming a second in-terferogram with one of the initial pair of images(see the dashed lines in Figure 3), or a secondpair entirely forms the reference interferogram.For instance, if the repeat interval for one of theinterferograms is one day, and the second’s is oneyear, we expect the deformation observable inthe short-time interferogram to be small. Thesame topographic signature would be present in

both. For a volcano, for example, if one pairspans an eruption and the other spans a quies-cent period, we assume the former will have thedeformation signal and the latter will not. A lin-ear combination of the two interferograms,based on the interferometric baselines appropri-ate for each, recovers the deformation signal.

An interferometric processorimplementation

Incorporating interferometric processing capa-bilities into a synthetic aperture radar processingsystem adds additional steps beyond the usualmatched filtering operation. Figure 4 shows a typ-ical flowchart. In the “range-Doppler” algorithm,raw radar measurements, typically comprising300-Mbyte data files and covering a 100-kmground region on a side, are first match-filteredto remove signal modulation and generate high-resolution signals in the across-track, or range, di-mension. We call this process range compression,because it takes echoes with a time or distance dis-persion of tens of kilometers and compressesthem to roughly 10 m in the range dimension.Resolution in the along-track, or azimuth, direc-tion follows from application of a second matchedfilter duplicating the “phase history” of the radarechoes we obtain from a series of pulses. An echo’sphase varies predictably as the distance from theradar to a point of the ground changes in thecourse of the radar platform’s motion. Because theenergy received from a scatterer on the groundvaries in both range and azimuth location duringacquisition, the data are first resampled so thatenergy from each different range is constrainedto one column of the data matrix. We can thenapply the matched filter as a series of 1D opera-tions rather than as 2D convolutions.

The image data for one of the two radar passesis additionally resampled during processing so thatsimilar ground points in both images coincide inlocation. Then, the interferogram is formed in thedata processor through point by point cross-multiplication of the two radar images.

Once the raw interferogram is formed, we of-ten must “unwrap” the interferogram phases—that is, resolve the 2π ambiguity in the phasemeasurements. This is needed because Equa-tions 1 through 3 require absolute phase values,and we must remove the inherent cyclic repeatsof phase measurements. Using precise estimatesof the interferometric baseline, we can separatetopographic effects from the deformationphases, if any, and we can use one or both sets of

δρ ρ ρ θ α+ ≈ − −( )∆ B B2

2sin

MAY/JUNE 2000 57

data in scientific analyses.We process the radar data in “patches”—that

is, a finite number of lines are read in and we ap-ply the 2D algorithms described earlier. Becausethe radar system can collect continuously alongits swath, we can process and stack end to endmany patches to form a wide image area. Thecalculations’ 2D nature implies that the most ef-ficient implementations result from the applica-tion of a single processor (or a small group ofprocessors) to each patch. We can process eachpatch completely independently from its neigh-bors as long as we accomplish the fusion into thefinal image using knowledge of any along-trackvariations in the interferometer baseline. Thus,we prefer coarse segmentation for parallel com-puting approaches, where each processor has ac-cess to a separate memory partition of at least400 Mbytes. More processors, each addressingsmaller memory partitions, usually result in littleincrease in throughput speed.

But this application is particularly well-suitedto distributed computation over networks, espe-cially if we use high-speed switched network sys-tems. At Stanford, we are distributing many suc-cessive patches over parallel Pentium III

workstations, each equipped with 512 Mbytes ofmemory. One master computer reads in a seriesof raw data records, sends it to the next availablemachine along with baseline information, andretrieves the processed interferogram pieces af-ter the calculations are complete so that we canmerge them into the final product. A typical 100× 100 km spaceborne radar image requires eightor nine patches, so that a large area on theground is inexpensively processed in the time ittakes to compute a single patch.

Deformation measurement examplesand their applications

Radar interferometry earthquake studies haveso far yielded detailed observations of crustalchange occurring during large earthquakes andof finer-scale phenomena resulting from waterflow through pores in deep rocks as it redistrib-utes when the forces within the crust seek a newequilibrium point after the large release of en-ergy after an earthquake (see Figure 5). Suchsubsurface flows can play an important role insetting up the conditions leading to future frac-tures and more seismic activity.

Rangecompression

AzimuthFFT

AzimuthFFT

Rangemigrationcorrection

Azimuthcompression

Rangecompression

Baselinedetermination

Phaseunwrapping

Rangemigration and

offset interpolation

Azimuthcompression

Offset data(a)

(b)

Measure offsets

May include Italian filter Rawinterferogram

Sometimes feedback

Topography tie points

Deformationtie points

Orbit 1raw data

Orbit 2raw data

Rawinterferogram

Synthetic interferogram(2 pass)

or2nd actual interferogram

Deformationestimator

Topographyestimator

Figure 4. (a)Syntheticaperture radarprocessingand raw interferogram generationflowchart; (b) interfero-metric post-processingyields bothtopographicand deforma-tion esti-mates.

58 COMPUTING IN SCIENCE & ENGINEERING

Volcanoes are another natural hazard that wecan study from minute shape changes. Volcaniceruptions can threaten local areas with lava, hotgases, and airborne debris. Explosions and mudflows are equally devastating, and the expulsionof airborne particles represents significant dan-ger for aircraft, because pyroclastic material canchoke or mechanically destroy a jet engine.

Figure 6 shows an example of a volcano appli-cation. Recent interferometric measurements re-veal strong localized uplift in a semicircular pat-

tern on the southwest flank of Fernandina vol-cano in the Galapagos Islands, where an erup-tion took place from January to April 1995. Theobservations show a maximum decrease in radarrange of 0.75 m, and they are consistent with amodel of a shallow-dipping dike intrusion feed-ing this fissure eruption. Sigurjon Jonsson andhis colleagues solved for a single rectangular dis-location dike source using nonlinear inversiontechniques and found that the best-fit dike is 3.8km long, 2.3 km high, and 0.86 m thick, with a34° dip.5 The total dike volume (0.0075 km3) is40% of the volume of extrusive materials esti-mated to have been produced during the erup-tion. The data do not permit a subvertical dike,implying that the least principal stress directionis not horizontal as researchers usually assume.

Another area of active investigation using in-terferometric radar techniques is the measure-ment of the speed of moving glaciers in Antarc-tica and Greenland (see Figure 7). The rate atwhich glacial ice is recirculated within theEarth’s water cycle affects global climate condi-tions. Whether or not ice is building up in thepolar caps and how much is discharged into thesea are significant inputs to global climate mod-els. Because the polar regions are remote and in-hospitable, as well as large, glacier flow velocitymeasurements are an ideal application for space-borne radar. The performance limitations hereare due more to the relatively high velocities ofthe glaciers and ice streams compared to platemotions, but researchers can circumvent thiswith more frequent observations so that the totaldisplacement between radar visits is similar.

Finally, surface subsidence phenomena such as

Figure 6. Anobserved ERSinterferogram(a) and subse-quently mod-eled (by Sigur-jon Jonsson)(b) displace-ment fieldfrom the em-placement ofa dike underthe flank ofFernandinavolcano, Galapagos Islands.5

Figure 5. Surface deformation observed by the ERS radar satellitesat very localized regions resulting from fluid flow near faultstructures in the months following the Landers earthquake.Numbered boxes highlight concentrations of deformation.4 Fieldtechniques would simply not measure these deformations unlesswe were lucky enough to have instrumentation set up in exactly theright place. (Figure courtesy of Gilles Peltzer, JPL.)

MAY/JUNE 2000 59

those from groundwater withdrawal are now al-lowing greater insight into resource managementproblems. In one study of the Las Vegas valley(see Figure 8),6 researchers have conclusivelydemonstrated structural control of subsidencebasins by quaternary faults with the new, high-spatial density measurements. Improved moni-toring capabilities will allow improved use andconservation of resources such as water and oil.

The scientific community has demon-strated radar interferometry’s appli-cation to these and other geophysi-cal problems only in the past 10

years. Many additional applications are in devel-opment, including measurements of subsidencefrom ground water retrieval or oil depletion.

The European Space Agency operates twosatellites, ERS-1 and ERS-2; the Canadians op-erate the Radarsat satellite; and the Japanese runJERS-1 (it’s now nonoperational). Although the

US orbited the first spaceborne radar, SEASAT,in 1978, NASA’s recent radar effort is in developingspace shuttle instruments. Because of the shuttle’slimited duration in orbit, we cannot easily adaptthese devices to crustal-deformation studies.

However, the US space agency is now consid-ering several new systems for free-flying inter-ferometric radar satellites. The existing interna-tional satellites were not originally designed forinterferometric operations and are not opti-mized for deformation observations—a newradar would be specifically designed for that pur-pose. In addition, NASA has supported devel-opment of the technique using aircraft equip-ment and also data collected by the internationalcollection of satellites. Because having interfer-ometric data available to the scientific commu-nity is a major factor in the advance of knowl-edge about many important geophysicalproblems, hopefully it will not be long before anoptimized radar producing large amounts ofcrustal-deformation data is in place.

References

1. D. Massonnet et al., “The Displacement Field of theLanders Earthquake Mapped by Radar Interfero-

Figure 7. Ice velocity measured using ERS interfer-ometry at each point on the Petermann Glacier,Greenland. (Image courtesy Eric Rignot, JPL.)

Figure 8. Subsidence of the region surrounding Las Vegas, Nevada,and its containment by quaternary faults.6

metry,” Nature, Vol. 364, No. 6433, 8 July 1993, pp.138–142.

2. A.G. Gabriel, R.M. Goldstein, and H.A. Zebker,“Mapping Small Elevation Changes over Large Ar-eas: Differential Radar Interferometry,” J. Geophys-ical Research, Vol. 94, No. B7, 10 July 1989, pp.9183–9191.

3. H.A. Zebker et al., “On the Derivation of Co-seis-mic Displacement Fields Using Differential RadarInterferometry: The Landers Earthquake,” J. Geo-physical Research, Vol. 99, No. 19, 1994, pp.617–619, 634.

4. G. Peltzer et al., “Postseismic Rebound in FaultStep-Overs Caused by Pore Fluid Flow,” Science,Vol. 273, No. 5279, 30 Aug. 1996, pp. 1202–1204.

5. S. Jonsson et al., “A Shallow-Dipping Dike Fed the1995 Flank Eruption at Fernandina Volcano, Gala-pagos, Observed by Satellite Radar Interferometry,”Geophys. Res. Lett., Vol. 26, No. 8, 15 April 1999,pp. 1077–1080.

6. F. Amelung et al., “Sensing the Ups and Downs ofLas Vegas: InSAR Reveals Structural Control of

Land Subsidence and Aquifer-System Deforma-tion,” Geology, Vol. 27, No. 6, June 1999, pp.483–486.

Howard A. Zebker is an associate professor of electri-cal engineering and geophysics at Stanford University.His research interests are in Earth crustal deformation,including measurements and subsurface processes,planetary exploration, interferometric radar remote-sensing techniques and applications, electromagneticpropagation and scattering, and digital signal pro-cessing. He received a BS in engineering and appliedscience from the California Institute of Technology, anMS in engineering from the University of California atLos Angeles, and a PhD in electrical engineering fromStanford University. He is an IEEE fellow, an AmericanGeophysical Union member, and an American Astro-nomical Society Division for Planetary Sciences affiliatemember. Contact him at Packard Building, Rm. 331,Stanford Univ., Stanford, CA 94305-9515; zebker@stanford.edu.

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President-Elect: BENJAMIN W. WAH *Past President: LEONARD L. TRIPP*VP, Educational Activities: JAMES H. CROSS II*VP, Conferences and Tutorials: WILLIS K. KING (1ST VP)*VP, Chapters Activities: WILLIAM W. EVERETTVP, Publications: SALLIE V. SHEPPARDVP, Standards Activities: STEVEN L. DIAMOND (2ND VP)*VP, Technical Activities: MICHEL ISREALSecretary: DEBORAH K. SCHERRER*Treasurer: THOMAS W. WILLIAMS2000–2001 IEEE Division V Director: DORIS L. CARVER1999–2000 IEEE Division VIII Director: BARRY W. JOHNSON2001–2002 IEEE Division VIII Director: BRUCE D. SHRIVERExecutive Director & Chief Executive Officer: T. MICHAEL ELLIOTT* voting member of the Board of Governors

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