IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013 375

Using EO-1 Hyperion Data as HyspIRI PreparatoryData Sets for Volcanology Applied to Mt Etna, Italy

Michael Abrams, Dave Pieri, Vince Realmuto, and Robert Wright

Abstract—One of the main goals of the Hyperspectral and In-frared Imager (HyspIRI) mission is to provide global observationsof surface attributes at local and landscape spatial scales (tens ofmeters to hundreds of kilometers) to map volcanic gases and sur-face temperatures, which are identified as indicators of impendingvolcanic hazards, as well as plume ejecta which pose risks to air-craft and people and property downwind. Our project has createdprecursor HyspIRI data sets for volcanological analyses, using ex-isting data over Mt. Etna, Italy. We have identified 28 EO-1 Hy-perion data acquisitions, and 12 near-coincident ASTER data ac-quisitions, covering six eruptive periods between 2001 and 2010.These data sets provide us with 30 m hyperspectral VSWIR dataand 90 m multispectral TIR data (satellite). They allowed us to ex-amine temporal sequences of several Etnaean eruptions. We ad-dressed the following critical questions, directly related to under-standing eruption hazards: 1) What do changes in SO emissionstell us about a volcano’s activity? How well do these measurementscompare with ground-based COSPEC measurements? 2) How dowe use measurements of lava flow temperature and volume to pre-dict advances of the flow front? 3) What do changes in lava laketemperatures and energy emissions tell us about possible eruptivebehavior?

Index Terms—Hyperspectral imaging, infrared image sensors,volcanic activity.

I. INTRODUCTION

I N 2004, the U.S. National Aeronautics and Space Ad-ministration commissioned the National Research Council

(NRC) to conduct a Decadal Survey for Earth science andapplications from space. The 2007 report is titled Earth Scienceand Applications from Space: National Imperatives for theNext Decade and Beyond [23]. The purpose of this study wasto provide NASA with a blueprint for the next 10 years, prior-itizing science missions, in response to community inputs andrecommendations. The report identified key science measure-ments and recommended a small number of missions to acquirethose measurements. The topical areas included Earth scienceapplications and societal benefits, and solid Earth hazards.Included in the recommended missions was the Hyperspec-

tral and Infrared Imager (HyspIRI) instruments that would pro-vide global observations at local and landscape scales (tens of

Manuscript received February 29, 2012; revised May 28, 2012 and July 18,2012; accepted August 28, 2012. Date of publication March 12, 2013; date ofcurrent version May 13, 2013. Work by Abrams, Realmuto, and Pieri was doneat the California Institute of Technology, Jet Propulsion Laboratory, under con-tract with the National Aeronautics and Space Administration.M. Abrams, D. Pieri, and V. Realmuto are with the Jet Propulsion Laboratory,

California Institute of Technology, Pasadena, CA 91109 USA.R. Wright is with the University of Hawaii, Honolulu, HI 96822 USA.Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSTARS.2012.2224095

meters to hundreds of kilometers) [2]. The mission would in-clude a hyperspectral visible-near infrared-shortwave infrared(VSWIR) imaging spectrometer, and a multispectral thermal in-frared (TIR) scanner. Operating from low earth orbit, the instru-ments would provide global coverage, frequent repeat observa-tions, and data at 60-m spatial resolution. One of the primary sci-ence goals of the mission is to map volcanic gases and surfacetemperatures, which are identified as indicators of impendingvolcanic hazards; as well as plume eject a which pose risksto aircraft and people and property downwind. The DecadalSurvey laid out a timeline to implement the missions: HyspIRIwas originally included in the second tier, with recommendedlaunch dates between 2013 and 2016, but the expected launchdate has been slipped to 2020 or later. NASA assigned prelimi-nary study activities for HyspIRI to the Jet Propulsion Labora-tory.The goal of our project was to create simulated HyspIRI vol-

canological data sets. Our primary focus was on Mount Etna,Sicily (Fig. 1), where we investigated active lava flows and vol-canic plumes. Mount Etna, a stratovolcano rising over 3300 me-ters above the Mediterranean, is one of the world’s most activevolcanoes, and its eruptions have been historically documentedfor longer than any other volcano [8], [33].Two styles of eruptive activity typically occur at Etna. Persis-

tent explosive eruptions, sometimes with minor lava emissions,take place from one or more of the three prominent summitcraters, the Central Crater, the Northeast (NE) Crater, and theSoutheast (SE) Crater (formed in 1978). These are often ac-companied by effusive plumes, containing water vapor, ash, andSO . Flank vents, typically with higher effusion rates, are activeless frequently and originate from fissures that open progres-sively downward from near the summit, usually accompaniedby strombolian eruptions at their upper ends (Fig. 2). Lavas andpyroclastics of alkali affinity make up the main bulk of Etna rep-resenting over 98% by volume of the volcano [8].Cinder cones are commonly constructed over the vents of

lower-flank lava flows. Lava flows extend to the foot of the vol-cano on all sides and have reached the sea over a broad areaon the SE flank. Highly explosive events produce tall eruptioncolumns (e.g., over 6 km high during the 2002 eruption [5], [6]),typically laden with both ash and SO . These are then trans-ported horizontally for hundreds of kilometers. All of the erup-tion phenomena pose hazards to property and life. For instance,there are numerous towns located on the volcano’s flanks, andthe nearby city of Catania has been inundated by lava numeroustimes in the past (e.g., the famous 1669 eruption [33]). Etna’sash plumes also pose direct hazard to aircraft over a large area,including those based at the military airfield at Sigonella, nearCatania, actively used by the U.S. Navy, NATO, and the Italian

1939-1404/$31.00 © 2013 IEEE

376 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

Fig. 1. Location map of Mt. Etna volcano, Sicily, Italy.

Fig. 2. Aerial view ofMt. Etna during simultaneous summit and flank eruptions(plume on right). Image taken from the International Space Station on October30, 2002.

Air Force; commercial airline traffic from the Fontanarossa air-field that serves Catania; and the Comiso airport.Mount Etna has been, and continues to be, a frequent target

for both satellite and aircraft optical instruments [1], [13], [14],[17], [18], [20], [27], [32], [37]. We assembled HyspIRI TIRdata from two instruments to create our simulated HyspIRI datasets (Table I). Hyperion began operation in early 2001 as the

TABLE IDESCRIPTION OF TWO SATELLITE SENSORS USED TO CREATE PRECURSOR

HYSPIRI TIR DATA SETS

VSWIR hyperspectral scanner on NASA’s Earth Observing-1(EO-1) satellite platform [38]. It continues to operate, acquiring196 useable data channels. The Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) has been ac-quiring data since 2000 on NASA’s Terra satellite platform[50]. ASTER combines multispectral VSWIR instrumentswith a multispectral TIR instrument. The combined data setsprovided us with 30-m hyperspectral VSWIR data and 90-mmultispectral TIR.We analyzed currently available data sets and tools to

address the following critical questions, directly related tounderstanding eruption hazards for configuring the HyspIRITIR: 1) What do changes in SO emissions tell us about a vol-cano’s activity? 2) How do we use measurements of lava flow

ABRAMS et al.: USING EO-1 HYPERION DATA AS HyspIRI PREPARATORY DATA SETS FOR VOLCANOLOGY APPLIED TO MT ETNA, ITALY 377

Fig. 3. Proposed HyspIRI TIR bandpass placements for eight channels.

temperature and volume to predict advances of the flow front?3) At what temperature should we set the saturation limits forHyspIRI’s TIR bands (Fig. 3)? For some of these questions wewere able to provide answers. For others, we find that currentcapabilities do not provide adequate data sets (due to temporallimitations) to fully answer the science questions. We have hadto content ourselves with showing how the techniques and toolswill one day allow us to advance our understanding more fully.

II. DATA SETS

The simulated HyspIRI TIR data sets over Mount Etna, Italywere assembled from EO-1 Hyperion data, and ASTER data.The five daytime data sets were assembled from the only five co-incident daytime ASTER+EO-1 data collection obtained duringthe eleven overlapping years of operations of the two instru-ments. They occurred on 15 January 2003, 19 July 2003, 11August 2003, 8 October 2008, and 30 June 2011. The singlenighttime data set was acquired 14 September 2004.Corresponding eruptive activity was reported by the Smith-

sonian Global Volcanism Program (http://www.volcano.si.edu/index.cfm), from which the following were extracted: On 15January 2003, ash emission increased at the 2750-m-elevationpyroclastic cone on the volcano’s upper south flank. There wasalso an associated increase in lava emission towards the south.On 19 July 2003, no activity was reported. On 11 August 2003,a weak, fluctuating glow was observed at the base of a dense gascolumn emitted from the NE Crater. On 10 September 2004, a300-m-long lava flow poured out from a vent; on 13 Septemberanother 1-km-long lava flow occurred. On 8 October 2008 and30 June 2011, no activity was reported.

III. LAVA FLOWS

The Hyperion data were scaled to radiance-at-the-sensor inunits of W/m sr m, resampled to 60-m pixel size; bands 1 to57 and 79 to 242 were combined to make 196-channel data sets,removing the overlap between Hyperion’s two spectrometers.ASTER’s five thermal bands were resampled to 60-m pixel size,scaled to radiance-at-the-sensor, and registered to the Hyperioncoverage using standard tie-point identification, and nearest-neighbor resampling. TheHyperion andASTER data were com-bined to a 201-band image. The data sets are in BSQ format,with ENVI text header files. The data sets can be downloaded

from the web site http://volcanology-hyspiri.jpl.nasa.gov/simu-lated-data.The nighttime Hyperion data sets from 12, 14, and 16

September 2004 were analyzed as part of a study to determinecooling rates of lava flows [43], [46], though only one of thesehad a matching ASTER data acquisition (Fig. 4). On the 12th,only a single flow (Flow I) existed with a flux of 441 MW. Onthe 14th, two flows were seen, I and II. Because of the high lavaflow temperature, coincidently acquired ASTER TIR pixelswere mostly saturated (Fig. 4(a)) for both flows. However,Hyperion data had most of the channels unsaturated (Fig. 4(b)),allowing calculation of radiant fluxes and convective heatflux (Fig. 4(c)). For September 14, total heat flux of lavaflow I was 761 MW, and lava flow II 161 MW. Two dayslater these flows had cooled and were putting out energies of384 MW and 57 MW respectively (flux measurements from[43], [46]).The general decrease in surface temperature was pronounced

between the 14th and 16th. This is explained by field observa-tions that the open lava channel had roofed over after the 14th.The combined Hyperion and ASTER daytime data set from

15 January 2003 was characterized by both ash emissions andlava flows. The flows, like those shown for 12 September 2004,were incandescent in visible wavelengths, and saturated somepixels at higher wavelengths in the Hyperion data (Fig. 5). Thismakes it difficult to accurately calculate energy fluxes from thelava flows; an instrument with much higher saturation levelswould be of great value. The ASTER TIR data are also saturatedfor most of the lava flow, and cannot be used to determine flowtemperatures. However, the spectral color of the emission plumeindicates that the major composition at this time was SO -rich(Fig. 6) (more about ASTER and SO later).

IV. SATURATION STUDIES

The surface temperature of an active lava body, be it a flow,a dome, or a lake, determines the rate at which the lava coolsand is an important boundary condition for estimating, for ex-ample, how quickly an active lava flow cools, rheologicallystiffens, and eventually ceases to flow [27]. Temporal varia-tions in the spectral radiance emitted from erupting volcanoeshas also been shown to act as a reliable proxy for eruption in-tensity (e.g., [19]), and has been shown to correlate with vari-ations in geophysical variables such as seismic energy releaseand degassing [49]. At Lascar volcano in Chile, simply usingsatellites to record how the spectral radiance emitted from itssummit crater varies over time has been demonstrated as a gen-uinely robust method for predicting when that volcano is likelyto erupt explosively (see [22], [26], and [41]).Although sensors such as the Landsat ThematicMapper (TM)

and its successor the Enhanced Thematic Mapper (ETM) havebeen used to quantify the surface temperature distributions ofactive lava flows (see [9], [12], [25], [27], [34], [48]) all havefailed to a certain degree. This is because these sensors were de-signed to observe spectral reflectance from targets such as vege-tation, shallow water, rocks and soils, and because of instrumentengineering constraints. As such, the upper measurement limitof these sensors (the maximum spectral radiance that a partic-ular waveband can measure, ) is optimized to observe tar-

378 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

Fig. 4. 14 September 2004 nighttime ASTER and Hyperion data for two lava flows. (a) Combined ASTER band 12 (red) and Hyperion (blue) data where mostASTER TIR pixels are saturated (bright red and white). (b) Color coded Hyperion 1.6 m data. (c) Radiant heat flux calculated from Hyperion data.

gets that reflect less than 100% of albedo (and for most targets,much less). They were not designed to image active lavas forwhich temperature can be as high as 1150 C (1423K) andfrom which emission of spectral radiance can be two orders ofmagnitude greater than Landsat TM can reliably measure [43],[46]. As a result, widespread sensor saturation has been noted asthe primary barrier to successfully resolving lava surface tem-peratures, and volcanogenic thermal fluxes, using these instru-ments.Although the Terra ASTER TIR sensor was designed with

volcanism in mind, the increased dynamic range of the sensorwhen operating in “volcano mode” offers only marginal im-provement over that afforded by the Landsat family [47]. Thesignificance of this is that if the spectral radiance emitted froma lava surface is unknown, then the common approaches to in-verting at-satellite spectral radiance to obtain surface tempera-ture (e.g., the dual-band method of [34]; the more complex mix-ture models of [44]) cannot be applied without great uncertainty.Neither can temporal variations in the energy emitted from anactive volcano be reliably quantified. An important advantage ofASTER data is to measure with high precision the backgroundtemperatures in volcanic areas, an important component in thethermal analysis of lave flows. Finally, neither ASTER nor Hy-perion instruments are capable of systematic monitoring of anyvolcanoes (Hyperion) or more than just a few (like ASTER), ascompared to Landsat. Temporal repeat coverage (like HyspIRI)is a critical capability.The baseline HyspIRI TIR design includes a middle infrared

waveband at 4 m, the purpose of which is to accurately

record the thermal radiance emitted from vegetation fires andactive volcanoes. Heritage for this capability lies in the designof the Terra and Aqua Moderate Resolution Imaging Spec-troradiometer (MODIS) sensors, which include an equivalentspectral band referred to as the “fire channel” [15]. Throughoutthe 1980s and 1990s the wildfire community suffered fromthe propensity of its orbital workhorse, the NOAA AdvancedVery High Resolution Radiometer (AVHRR) series, to easilysaturate over high temperature fires. As a result, the MODISfire channel (MODIS band 21) was designed to saturate at pixelintegrated temperatures of 506K and 478K, on Terra and Aquarespectively, compared to temperatures of 320K for AVHRR.Therefore, the MODIS fire channels have demonstrated greatresilience to saturation over vegetation fires. It has also beenshown to represent a marked improvement over AVHRR forstudying active volcanism (e.g., [44]). The 4- m region is ofspecial significance for both fire and volcano remote sensing.Over the temperature range 600–1500K (covering the rangeof active lavas) emitted spectral radiance at 4- m increasesapproximately with the fourth power of temperature [42]. Asthe radiant exitance from a surface also increases with thefourth power of temperature (the Stefan Boltzmann law) a mea-surement of spectral radiance from an active lava at 4 m canbe converted directly to an estimate of power loss (in W/m ;see [42] for a derivation) without the need to unravel the lava’sdetailed sub-pixel surface temperature distribution.Although the specifications of the MODIS fire channels have

virtually eliminated the incidence of saturation over both firesand active lavas, in has been suggested that the for this

ABRAMS et al.: USING EO-1 HYPERION DATA AS HyspIRI PREPARATORY DATA SETS FOR VOLCANOLOGY APPLIED TO MT ETNA, ITALY 379

Fig. 5. 15 January 2003 Hyperion daytime data. Bands 27 (0.620 m), 57 (0.925 m), and 84 (1.194 m) are in RGB. The plots show radiance at the sensor asa function of wavelength for lava pixels at three different temperatures.

channel was set too high, resulting in a lack of precision whenobserving in the temperature range at which the majority ofwildfires burn [10]. An objective of this study was to preventa similar situation arising with the HyspIRI TIR. To this endwe have simulated the response of the HyspIRI TIR to a rangeof volcanic targets to determine the most appropriate value of

to ensure that it is high enough to provide unsaturateddata over active lavas, without being too high, and in effect“wasting bits”.The first step in simulating the response of the HyspIRI 4 m

TIR channel to active lavas is to determine the range of sur-face-leaving radiance that real lavas exhibit at the scale of a60-m instantaneous field of view, at this wavelength. To do this,we used data acquired by the EO-1 Hyperion sensor to deter-mine the surface temperature distributions for a suite of ac-tive lavas. The fact that Hyperion measures a contiguous spec-

trum between 0.4 and 2.2 m means that as the tempera-ture of an active lava increases, the corollary of saturation (andincreasing numbers of unusable wavebands) at longer wave-lengths is increasing amounts of emitted thermal radiance (anduseable data) at increasingly shorter wavelengths. In this way,Hyperion provides sufficient unsaturated spectral radiance mea-surements for complex sub-pixel radiance-to-temperature mix-ture models to be employed, even for the very hottest active lavasurfaces. These more complex mixture models allow a more ac-curate determination of lava surface temperature distributions,and hence surface leaving radiance, than those attainable usingthe “dual-band” method (see [44]). This allows the surface tem-perature distribution of active lavas, and consequently the sur-face-leaving radiance at the 30-m scale, to be determined ina manner not possible with Landsat/ASTER-class data. Oncethe sub-pixel temperature distribution is resolved using data ac-

380 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

Fig. 6. 15 January 2003 ASTER TIR color composite of Etna emission plume.The yellow color streaming from the summit to the northwest indicates the pres-ence of SO gas due to spectral absorption of SO in the ASTER bands. Whitepixels are saturated. Bands at 11.3 m, 10.6 m, 8.6 m are displayed in RGBafter a decorrelation stretch enhancement.

quired in the 0.4–2.2 m region, the emitted spectral radianceat other wavelengths can be predicted.The method for retrieving lava surface temperatures from

Hyperion data is described and validated in [43], [46]. Briefly,the spectral radiance emitted from a lava surface can bewritten simply as

where is the temperature of the th component, is the wave-length, is the number of components, and is the fraction ofthe sensor’s instantaneous field of view that it occupies. Sat-urated wavebands were excluded, the remaining spectral mea-surements were corrected for the non-unitary, graybody emis-sivity of the lava (here a value of 0.95 was assumed for basalt)and absorption of the surface-leaving radiance by the atmos-phere (estimated using the MODTRAN radiative transfer code,assuming an appropriate atmosphere, season, and altitude forEtna). Nighttime data were used to avoid contamination of theemitted radiance by reflected sunlight.The best-fit solution of equation (1) to each spectral radiance

spectrum was then obtained, to yield a solution for eachimage pixel, using the Levenberg-Marquardt method. wasallowed to float between limits of 100 C (the theoretical lowersensitivity limit of Hyperion) and 1200 C (beyond the basaltliquidus), while was allowed to float between values of 0 and1. The sum of the values was constrained to not exceed onebut could (and usually did) sum to less than one. The remainderof the pixel was assumed too cool to radiate significantly at thesewavelengths, and was set to a background temperature of 8 C,appropriate for Mount Etna’s flank region. (The backgroundtemperature cannot be computed using Hyperion data becausethe spectrometer is not sensitive to emission from surfaces witha whole pixel at 900 m , temperature of 90 C.) [43], [46]found that most solutions converged when 2 or 3. The so-lution with the lowest root mean square error was retained. The

set of and values that result allow the surface-leaving radi-ance, at 4 m and a spatial resolution of 30 m, to be predicted.Fig. 7 shows the results obtained for a sequence of images of

lava flows erupted at Mount Etna in late 2004.On the left is the simulated 4- m brightness temperature

at 30-m resolution calculated from Hyperion data using themethodology described; on the right, the simulated responseof HyspIRI’s 4 m channel at 60-m spatial resolution. The60-m data were derived from the 30-m data. Spatial resamplingassumed the point spread function of Landsat TM’s longwaveinfrared channel (albeit at 60 m) which, like HyspIRI TIR’sinfrared subsystem is a whiskbroom scanning instrument. Thespectral response function of MODIS band 21 was assumedto perform the spectral resampling. Although the images havebeen rotated so north is to the top, they have not been geo-reg-istered.Channel-fed aa lava flows, which are much longer than they

are wide and are characterized by lava flowing in a channel con-fined by lateral levees that are much cooler than the lava inthe channel itself, are common at Mount Etna, much more sothan the rather amorphous pahoehoe flow-fields characteristicof Kilauea volcano in Hawaii. For comparison, Fig. 8 showsan equivalent result obtained from analysis of a Hyperion scenefor a much larger lava flow erupted at Nyamuragira, a volcanoin the Democratic Republic of Congo. Comparison of the sim-ulated HyspIRI 4- m TIR data with the raw 30-m data revealsthat while the central lava channel was somewhat resolved inthe 30-m Hyperion data, it becomes indistinct at 60 m. Thus, forthis flow, while the HyspIRI TIR would allow for cooling fromthe lava flow as a whole to be determined the spatial resolutionwould be insufficient to allow the cooling from the channeledlava itself to be estimated. To place this in context, however,this flow is much smaller than those that are typically of con-cern at Mount Etna insofar as the hazards the volcano poses.Fig. 8 shows how a more substantial lava flow erupted at Nya-muragira volcano would appear in the HyspIRI TIR data. Thisflow is more comparable in size to those erupted at Etna in1983, 1991–1993, 2001, 2006, 2008–2009, flows which in sev-eral cases have either destroyed infrastructure or threatened in-frastructure to the point that artificial lava diversion measureswere taken. The results presented here indicate that for a flowin this size range (i.e., 5 km long and greater) the 60-m spatialresolution of HyspIRI will be sufficient to estimate the coolingrate of the feeder channel itself.Obviously, as the data are resampled from 30 to 60 m, so

themaximum predicted 4- m brightness temperature decreases.For the purposes of this study, we are interested in how high

(or expressed as an equivalent whole pixelbrightness temperature) should be, to ensure that saturation ofHyspIRI’s 4- m TIR channel does not occur over the active lavaflow. For the seven Etna data sets depicted in Fig. 7, a of700K would ensure that no regions of the flow surface would besufficiently radiant to saturate the HyspIRI 4- m TIR channelat 60 m. However, as a recommendation regarding thenecessary to ensure that HyspIRI provides unsaturated data overa wide range of active lava bodies, the Etna example providesinsufficient information, given that the flow itself was relativelysmall. Fig. 9 shows how the incidence of saturation decreasesas increases for the much more substantial Nyamuragira

ABRAMS et al.: USING EO-1 HYPERION DATA AS HyspIRI PREPARATORY DATA SETS FOR VOLCANOLOGY APPLIED TO MT ETNA, ITALY 381

Fig. 7. The development of a lava flow at Mt. Etna, Sicily is shown in a time sequence of seven Hyperion images (i)–(vii) using the 4- m band. These are shownon the let for native 30-m Hyperion resolution using the method described, paired (on the right) with simulated HyspIRI TIR 4- m images, both spatially (60 m)and spectrally. Hyperion images used to generate these results were acquired on (i) 12 September 2004, (ii) 14 September 2004, (iii) 16 September 2004, (iv) 23September 2004, (v) 7 October 2004, (vi) 9 October 2004, and (vii) 3 December 2004.

Fig. 8. Simulated HyspIRI 4- m image of a lava flow at Nyamuragira volcano,Democratic Republic of Congo. Color bar gives the 4- m brightness tempera-ture for each pixel (in Kelvin). On the left is the 4- m brightness temperatureestimated from Hyperion data. On the right, the same data resampled spatiallyand spectrally to match the proposed specifications of HyspIRI. The Hyperionimage used to generate this result was acquired on 20 May 2004.

example. For this lava flow, a 4- m of 1000K would berequired to ensure that the HyspIRI TIR provides unsaturateddata at all points on the flow surface.

Fig. 9. Histogram showing the number of lava flow pixels predicted to satu-rate HyspIRI’s 4- m band as a function of , for the Nyamuragira exampledepicted in Fig. 7.

V. SO STUDIES

The utility of ASTER and MODIS data in studies of vol-canic SO plumes is evident from the quantity and variety ofpublished investigations (e.g., [4], [16], [24], [29], [39], [40] or[51]. The use of ASTER data is an ideal preparatory activity

382 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

Fig. 10. Comparison of the TIR spectral response of ASTER,MODIS, and the HyspIRI TIR to the transmission spectra of SO and water vapor H O . (a) ASTERvs. SO , (b) MODIS vs. SO , (c) Notional HyspIRI vs. SO2, and (d) Notional HyspIRI vs. H O.

for HyspIRI-based SO mapping, due to the similarities in thespectral response (Fig. 10).In addition, the 60-m spatial resolution planned for HyspIRI

is similar to the 90-m resolution of ASTER. High spatial reso-lution increases our sensitivity to SO as the fraction of a pixeloccupied by a plume increases with increasing spatial resolution(i.e., decreasing IFOV).The heritage for the HyspIRI TIR channels is shown in

Fig. 10. The transmission spectrum of SO is superimposedon the spectral response of ASTER (Fig. 10(a)), MODIS(Fig. 10(b)), and the notional spectral response of the HyspIRITIR (Fig. 10(c)). In Fig. 10(d) we superimpose the transmissionspectrum of water vapor H O on the HyspIRI TIR spectralresponse. The H O absorption, together with that of CO(not shown here), define the 8–12 m “atmospheric window”traditionally used in TIR remote sensing of the earth’s surface.The transmission spectra shown in Fig. 10 represent a nadirview, or vertical optical path, from the top of the atmosphere(altitude 100 km) to sea level.The spectrum of SO between 7 and 13 m is distinguished

by a strong absorption band centered near 7.3 m, a weakerband centered near 8.5 m, and transparency at wavelengths be-tween 9.5 and 13 m. ASTERChannels 10, 11, and 12 cover the8.5 m absorption band (Fig. 10(a)), and MODIS Channels 28and 29 cover the 7.3 and 8.5 m SO absorption bands, respec-tively (Fig. 10(b)). Accordingly, the HyspIRI TIR channels atwavelengths less than 10 m are based on MODIS Channel 28

and ASTER Channels 10, 11, and 12 (Fig. 10(c)). Regarding the7.3 m channel, we note that strong H O absorption at wave-lengths 8.0 m (Fig. 10(d)) will degrade the sensitivity of thischannel to SO plumes at altitudes less than 5 km (cf. [28]).Figs. 11 and 12 are examples of SO abundance maps derived

from ASTER data acquired on a daytime overpass on 29 July2001 and a nighttime overpass on 6 June 2000, respectively.The data depicted in Fig. 11 were acquired during an eruptionthat began on 17 July with the opening of new fissures on thenortheastern and southern slopes of the edifice, between the el-evations of 2500–3000 meters, and the effusion of lava fromthese fissures (cf. [36]).The effusive activity continued until the end of the eruption

on 9 August 2001. At the time of the ASTER overpass the erup-tion was characterized by Strombolian and Hawaiian explosionsat the lower end (2740-m elevation) of the southern fissure.Fig. 11(a) is the false-color composite of the ASTER visible

and near infrared (VNIR) data. The view of the active lava flowwas obscured by steam clouds that formed over the southernfissure. Fig. 11(b) is a color-composite of ASTER TIR data fromChannels 14, 13, and 11 (Fig. 10(a)) displayed in red, green,and blue, respectively. To enhance spectral contrast, the ASTERdata were processed with the de-correlation stretch algorithm[11] prior to the compositing.The SO plume appears yellow in the TIR color composite

(Fig. 11(b)), while the steam clouds, and associated veil of waterdroplets, appear blue [40]. The steam clouds obscured the view

ABRAMS et al.: USING EO-1 HYPERION DATA AS HyspIRI PREPARATORY DATA SETS FOR VOLCANOLOGY APPLIED TO MT ETNA, ITALY 383

Fig. 11. Analysis of ASTER data acquired during a daytime overpass of MountEtna on 29 July 2001. (A) False-color composite of ASTER VNIR data aggre-gated from a native resolution of 15-m to the 90-m resolution of the ASTERTIR; (B) Color-composite of the data from TIR Channels 14, 13 and 11 (dis-played RGB) where the SO plume appears yellow and droplets of liquid water(as opposed to water vapor) appear in blue; and (C) Map of SO column abun-dance derived from ASTER TIR.

Fig. 12. Analysis of ASTER data acquired during a nighttime overpass ofMount Etna on 6 June 2000. (a) Color-composite of the data from TIR Channels14, 13 and 11, (displayed as RGB) where the SO plume appears yellow anddroplets of liquid water (as opposed to water vapor) appear in blue. (b) Map ofSO column abundance derived from ASTER TIR.

of the SO plume, indicating that the clouds were at a higher al-titude than the plume. The wind field at this altitude transportedthe veil of water droplets to the south, while the wind field at theplume altitude transported the plume to the southeast. The SOplume was sheared to the south as it crossed the coast, presum-ably due to the presence of on-shore winds.Fig. 11(c) is a map of the SO column abundance (mass per

unit area), which is the product of the SO concentration (massper unit volume) and plume thickness. The fundamentals ofthe retrieval procedures were described in [30] and [31]. Themaximum abundances were 6 g/m , although there may havebeen higher concentrations of SO in the portion of the plumeobscured by the steam clouds.The ASTER data depicted in Fig. 12 were acquired during a

long-lived episode of activity characterized by lava fountains,lava extrusions, Strombolian eruptions, and high gas emissions.This episode began on 26 January and ended on 24 June 2000(cf. [3]). The data were acquired immediately after the 61st (of64) lava fountain event on 5 June 2000, which was accompaniedby strong gas emissions.Fig. 12(a) is a color-composite of the ASTER TIR data, cre-

ated with the same procedures described for Fig. 11(a). TheSO abundance map, Fig. 12(b), captures the remnants of thehigh-emission event of 5 June 2000. The maximum abundanceswere 6 g/m , with abundances in excess of 8 g/m recordednearest to the summit craters. A comparison of the SO gener-ated from day- and nighttime overpasses (Figs. 11(c) and 12(b),respectively) indicates that our sensitivity to low concentrations

of SO was not affected by the lower land surface temperaturesencountered during the nighttime overpass.We selected 184 ASTER scenes of Mount Etna acquired be-

tween 2000 and 2010 in an effort to construct a time-seriesof SO emissions from the summit craters. Our initial criteriafor selection were clear views of the summit region during pe-riods of surface activity. We applied the de-correlation stretch toeach scene and selected 30 scenes for plume mapping based onviewing conditions and the presence of SO plumes. This lowyield ( 16%) for candidate scenes limits the utility of ASTERdata to monitor changes in SO emission rates on time scalesfrom weeks to months. We will expand our search to includescenes with no evidence of surface activity. This new searchwill increase the number of candidate scenes for plume map-ping, but we do not expect an increase in yield.The HyspIRI TIR will provide more frequent, consistent re-

peat coverage than either ASTER or Hyperion, so more goodscenes will be available. HyspIRI’s 60-m spatial resolution willbe better than ASTER’s 90 m, and improved spectral bands willallow better mapping of plume constituents.

VI. CONCLUSIONS

Our main objective in creating these simulated HyspIRI TIRdata sets was to begin to illustrate the benefits to scientificanalyses from the putative acquisition of multispectral TIRdata by HyspIRI at over 2.7 times higher areal resolutionas compared with ASTER TIR data (e.g., 3600 m pixelversus 8100 m pixel), and with an expanded TIR bandpass(7.3–12 m) vs. ASTER (8–12 m). As a result of this study,we are able to draw several of conclusions that suggest HyspIRITIR data will provide science advantages as compared to sim-ilar analyses possible with ASTER TIR data.1) First, as a result of this effort, we were able to viablysimulate HyspIRI TIR-like data using EO-1 Hyperion andASTER TIR data, creating new HyspIRI-like hybrid datathat contained a band at 4 m and seven additional bandsranging from 7.3 m to 12 m.

2) As shown here, because of increased spectral resolutionand spatial resolution, including a much higher thermalsaturation temperature than was possible with ASTER,HyspIRI TIR will improve upon existing ASTER observa-tions of volcanic gas and ash emissions, allowing greatersensitivity and lower detection limits for SO plumes, andbetter delineation of thermally emitting areas of activelava flows.

3) Improved spatial resolution (e.g., 60 m/pixel for HyspIRIversus 90 m/pixel for ASTER) will materially help in de-lineating the correspondence between flow morphologyand radiantly emitting areas of lava flows. This is criticallyimportant to better understand rheo-thermal boundary con-ditions, thus improving flow dynamic models for lava flowhazard estimations. For instance, a HyspIRI TIR thermalsaturation temperature set at 700K for the 4- m channelappears to be adequate to prevent sensor saturation. Whilefor small open-channel aa lava flows (typical for Etna) ther-mally prominent central channels cannot typically be re-solved at 60 m/pixel, for larger flows (e.g., Nymiragira and

384 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

possibly for future Mauna Loa eruptions), central chan-nels are (or will be) visible, because they are significantlyhotter than confining levees and flow aprons in the lessthermally mixed (i.e., higher spatial resolution) HyspIRITIR pixels. Such delineations are important in determiningeffusion rate and predicting ultimate flow run-out lengths,both parameters of crucial importance for mitigating lavaflow hazards.

4) For volcanogenic SO studies, the higher spatial resolutionof HyspIRI vs. ASTER and inclusion of the additional TIRband at 7.3 m (despite increased water vapor presenceat altitudes below 5 km ASL vs. longer wavelengths) willprovide increased sensitivity for detection and mapping ofboth passively and actively emitted cold volcanic plumesin the lower troposphere (e.g., PuuOo plume in Hawaii).Such detections are important in measuring increased ordecreased passive degassing rates as potential eruption pre-cursors.

5) HyspIRI TIR’s 2.5-day repeat at the equator (includesboth day and night observations), and greater frequencyat higher latitudes is a major improvement over ASTER,EO-1 and Landsat. This will provide many more views ofactive volcanoes at better spatial resolution than ASTER.

Overall, we feel that the increased spatial resolution and ad-dition of 4- m and TIR bands that will be part of the HyspIRITIR design offer substantial demonstrated benefits for analysesand hazard evaluations of volcanogenic thermal activity and ofpassive (and active) tropospheric sulfur dioxide emissions.

REFERENCES

[1] M. Abrams, R. Bianchi, and D. Pieri, “Revised mapping of lava flowson Mount Etna, Sicily,” Photogramm. Eng. Remote Sens., vol. 62, pp.1353–1359, 1996.

[2] M. Abrams and S. Hook, “HyspIRI: Hyperspectral and Infrared Im-ager,” in Thermal Remote Sensing: Sensors, Methods, Applications, C.Kunzer and S. Dech, Eds. New York: Springer, 2012.

[3] S. Alparone, D. Andronico, L. Lodato, and T. Sgroi, “Relationship be-tween tremor and volcanic activity during the southeast crater eruptionon Mount Etna in early,” J. Volcanol. Geotherm. Res., vol. 108, no. B5,p. 2241, 2003, 10.1029/2002JB001866.

[4] R. Campion, G. Salerna, P.-F. Coheur, D. Hurtmans, L. Clarisse, K.Kazahaya, M. Burton, T. Caltabiano, C. Clerbaux, and A. Bernard,“Measuring volcanic degassing of SO in the lower troposphere withASTER band ratios,” J. Volcanol., Geotherm. Res., vol. 194, pp. 42–54,2010.

[5] S. Carn, L. L. Strow, de Souza-Machado, Y. Edmonds, and S. Hannon,“Quantifying tropospheric volcanic emissions with AIRS: The 2002eruption of Mt. Etna (Italy),” Geophys. Res. Lett., vol. 32, p. L02301,2005, 10.1029/2004GL021034.

[6] S. Carn, L. L. Strow, de Souza-Machado, Y. Edmonds, and S. Hannon,“Quantifying tropospheric volcanic emissions with AIRS: The 2002eruption of Mt. Etna (Italy),” Geophys. Res. Lett., vol. 32, p. L02301,2005, 10.1029/2004GL021034.

[7] J. Cipar, G. P. Anderson, and T. W. Cooley, “Temperature and poweroutput of the lava lake in Halema’uma’u crater, Hawaii, using a space-based hyperspectral imager,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 5, pp. 617–624, 2012.

[8] D. Chester, A. Duncan, J. Guest, and K. Kilburn, Mount Etna: TheAnatomy of a Volcano. Stanford, CA: Stanford Univ. Press, 1985.

[9] S. Donegan and L. P. Flynn, “Comparison of the response of theLandsat 7 enhanced thematic mapper plus and the Earth Observing-1advanced land imager over active lava flows,” J. Volcanol. Geotherm.Res., vol. 135, pp. 105–126, 2004.

[10] B. C. Gao, X. X. Xiong, R. R. Li, and D. Y. Wang, “Evaluation ofthe moderate resolution imaging spectrometer special 3.95- m firechannel and implications on fire channel selections for future satelliteinstruments,” J. Appl. Remote Sens., vol. 1, 2007, 10.1117/1.2757715.

[11] A. Gillespie, A. Kahle, and R. Walker, “Color enhancement of highlycorrelated images, I, decorrelation and HSI contrast stretches,” RemoteSens. Environ., vol. 20, pp. 209–235, 1986.

[12] A. Harris, L. P. Flynn, L. Keszthelyi, P. J. Mouginis-Mark, S. K. Row-land, and J. A. Resing, “Calculation of lava effusion rates from LandsatTM data,” Bull. Volcanol., vol. 60, pp. 52–71, 1998.

[13] A. Harris, S. Blake, D. Rothery, and N. Stevens, “A chronology of the1991 to 1993 Mount Etna eruption using advanced very high resolu-tion radiometer data: Implications for real-time thermal volcano mon-itoring,” J. Geophys. Res., vol. 102, pp. 7985–8003, 1997.

[14] A. Harris, F. Massimiliano, and R. Wright, “Hazard assessmentof Mount Etna using a hybrid lava flow inundation model andsatellite-based land classification,” Natural Hazards, vol. 58, pp.1001–1027, 2011.

[15] Y. Kaufman, C. O. Justice, L. P. Flynn, J. D. Kendall, E. M. Prins, L.Giglio, D. Ward, W. P. Menzel, and A. W. Setzer, “Potential globalfire monitoring from EOS-MODIS,” J. Geophys. Res., vol. 103, pp.32215–32238, 1998.

[16] C. Kearney, K. Dean, V. Realmuto, I. Watson, J. Dehn, and F. Prata,“Observations of SO production and transport from Bezymiannyvolcano, Kamchatka using the moderate resolution infrared spectro-radiometer (MODIS),” Int. J. Remote Sens., vol. 29, pp. 6647–6665,2008.

[17] V. Lombardo and M. Buongiorno, “Lava flow thermal analysis usingthree infrared bands of remote sensing imagery: A study case fromMount Etna 2001 eruption,” Remote Sens. Environ., vol. 101, pp.141–149, 2006.

[18] V. Lombardo, F. Buongiorno, L. Merucci, and D. Pieri, “Differencesin Landsat TM derived lava flow thermal structure during summit andflank eruptions at Mount Etna,” J. Volcanol. Geotherm. Res., vol. 134,pp. 15–34, 2004.

[19] J. Lyons, G. P. Waiter, W. I. Rose, and G. Chinga, “Patterns in openvent, Strombolian behavior at Fuego volcano, Guatemala, 2005–2007,”Bull. Volcanol., vol. 72, pp. 1–15, 2009.

[20] A. Krueger, N. A. Krotkov, K. Yang, S. Carn, G. Vicente, and W.Schroeder, “Applications of satellite-based sulfur dioxide monitoring,”IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol.2, pp. 293–298, 2009.

[21] F. Marchese, N. Pergola, and L. Telesca, “Investigating the temporalfluctuations in satellite advanced very high resolution radiometerthermal signals measured in the volcanic area of Etna (Italy),” Fluctu-ation and Noise Lett., vol. 6, pp. L305–L316, 2006.

[22] S. Matthews, M. C. SGardeweg, and R. S. J. Sparks, “The 1984 to1996 cyclic activity of Lascar volcano, northern Chile: Cycles of domegrowth, dome subsidence, degassing, and explosive eruptions,” Bull.Volcanol., vol. 59, pp. 72–82, 1997.

[23] National Research Council, 2007, “Earth Science and ApplicationsFrom Space: National Imperatives for the Next Decade and Beyond,”Committee on Earth Science and Applications from Space: A Com-munity Assessment and Strategy for the Future, National ResearchCouncil, 0-309-66714-3, 456 pages.

[24] M. Novak, I. Watson, H. Delgado-Granados, W. Rose, L. Cardenas-Gonzalez, and V. Realmuto, “Volcanic emissions from Popocatepetlvolcano, Mexico, quantified using moderate resolution imaging spec-troradiometer (MODIS) infrared data: A case study of the December2000–January 2001 emissions,” J. Volcanol. Geotherm. Res., vol. 170,pp. 76–85, 2008.

[25] C. Oppenheimer, “Lava flow cooling estimated from Landsat thematicmapper infrared data: The Lonquimay eruption (Chile, 1989),” J. Geo-phys. Res., vol. 96, pp. 21,865–21,878, 1991.

[26] C. Oppenheimer, P.W. Francis, D. A. Rothery, R.W. T. Carlton, and L.S. Glaze, “Infrared image analysis of volcanic thermal features: LascarVolcano, Chile, 1984–1992,” J. Geophys. Res., vol. 98, pp. 4269–4286,1993.

[27] D. Pieri, L. Glaze, and M. Abrams, “Thermal radiance observation ofan active lava flow during the June 1984 eruption of Mount Etna,”Geology, vol. 18, pp. 1018–1022, 1990.

[28] A. Prata, W. Rose, S. Self, and D. O’Brien, “Global, long-term sulphurdioxide measurements from TOVS data: A new tool for studying ex-plosive volcanism and climate,” Geophys. Monograph, vol. 139, pp.75–92, 2003.

[29] S. Pugnaghi, G. Gangale, S. Corradini, and M. F. Buongiorno, “Mt.Etna sulfur dioxide flux monitoring using ASTER-TIR data and at-mospheric observations,” J. Volcanol. Geotherm. Res., vol. 152, pp.74–90, 2006.

[30] V. Realmuto, A. Sutton, and T. Elias, “Multispectral thermal infraredmapping of sulfur dioxide plumes: A case study from the East RiftZone of Kilauea Volcano, Hawaii,” J. Geophys. Res., vol. 102, pp.15,057–15,072, 1997.

ABRAMS et al.: USING EO-1 HYPERION DATA AS HyspIRI PREPARATORY DATA SETS FOR VOLCANOLOGY APPLIED TO MT ETNA, ITALY 385

[31] V. Realmuto and H. Worden, “Impact of atmospheric water vaporon the thermal infrared remote sensing of volcanic sulfur dioxideemissions: A case study from the Pu’u ‘O’o vent of Kilauea Volcano,Hawaii,” J. Geophys. Res., vol. 105, pp. 21,497–21,508, 2000.

[32] M. Rix, P. Valks, N. Hao, J. van Geffen, C. Clerbaux, L. Clarisse, P.-FCoheur, R. D. G. Loyola, T. Erbertseder, W. Zimmer, and S. Emmadi,“Satellite monitoring of volcanic sulfur dioxide emissions for earlywarning of volcanic hazards,” IEEE J. Sel. Topics Appl. Earth Observ.Remote Sens. (JSTARS), vol. 2, pp. 196–206, 2009.

[33] G. Rodwell, Etna, A History of the Mountain and Its Eruptions.London, U.K.: C. Kegan Paul & Co., 1878, 218 pp.

[34] D. Rothery, P. W. Francis, and C. A.Wood, “Volcano monitoring usingshort wavelength infrared satellite data,” J. Geophys. Res., vol. 93, pp.7993–8008, 1988.

[35] A. Rybin, M. Chibisova, P. Webley, T. Steensen, P. Izbekova, C. Neal,and V. Realmuto, “Satellite and ground observations of the June 2009eruption of Sarychev peak volcano, Matua Island, Central Kuriles,”Bull. Volcanol., vol. 73, pp. 1377–1392, 2011.

[36] S. Scollo, P. Del Carlo, and M. Coltelli, “Tephra fallout of the 2001Etna flank eruption: Analysis of the deposit and plume dispersion,” J.Volcanol. Geotherm. Res., vol. 160, pp. 147–164, 2007.

[37] S. Teggi, M. Bogliolo, and M. Buongiorno, “Evaluation of SO emis-sion fromMount Etna using diurnal and nocturnal multispectral IR andvisible imaging spectrometer thermal IR remote sensing images and ra-diative transfer models,” J. Geophys. Res., vol. 104, pp. 20069–20079,1999.

[38] S. Ungar, J. Pearlman, and J. Mendenghall, “Overview of the EarthObserving One (EO-1) mission,” IEEE Trans. Geosci. Remote Sens.,vol. 41, pp. 1149–1159, 2003.

[39] M. Urai, “Sulfur dioxide flux estimation from volcanoes using ad-vanced spaceborne thermal emission and reflection radiometer—Acase study of Miyakejima volcano, Japan,” J. Volcanol. Geotherm.Res., vol. 132, pp. 1–13, 2004.

[40] M.Watson, V. Realmuto,W. Rose, A. Prata, G. Bluth, Y. Gu, C. Bader,and T. Yu, “Thermal infrared remote sensing of volcanic emissionsusing the moderate resolution imaging spectroradiometer,” J. Volcanol.Geotherm. Res., vol. 135, pp. 75–89, 2004.

[41] M. Wooster and D. A. Rothery, “Thermal monitoring of Lascar vol-cano, Northern Chile, using infrared data from the along track scanningradiometer,” Bull. Volcanol., vol. 58, pp. 566–579, 1997.

[42] M.Wooster, B. Zhukov, and D. Oertel, “Fire radiative energy for quan-titative study of biomass burning: Derivation from the BIRD experi-mental satellite and comparison toMODIS fire products,”Remote Sens.Environ., vol. 86, pp. 83–107, 2003.

[43] R.Wright, H. Garbeil, and A. Davies, “Cooling rate of some active lavaflows determined using an orbital imaging spectrometer,” J. Geophys.Res., vol. 115, pp. B06205–B06218, 2010.

[44] R. Wright and L. Flynn, “On the retrieval of lava-flow surface tem-peratures from infrared satellite data,” Geology, vol. 31, pp. 893–896,2003.

[45] R. Wright and L. P. Flynn, “Space-based estimate of the volcanic heatflux into the atmosphere during 2001 and 2002,” Geology, vol. 32, pp.189–192, 2004.

[46] R. Wright, H. Garbeil, and A. G. Davies, “Cooling rate of some activelavas determined using an orbital imaging spectrometer,” J. Geophys.Res., vol. 115, p. B06205, 2010, 10.1029/2009JB006536.

[47] R. Wright, D. A. Rothery, S. Blake, A. J. L. Harris, and D. C. Pieri,“Simulating the response of the EOS Terra ASTER sensor to high-tem-perature volcanic targets,”Geophys. Res. Lett., vol. 26, pp. 1773–1776,1999.

[48] R. Wright, D. A Rothery, S. Blake, and D. C. Pieri, “Improved remotesensing estimates of lava flow cooling: A case study of the 1991 to 1993Mount Etna eruption,” J. Geophys. Res., vol. 105, pp. 23681–23694,2000.

[49] R. Wright, S. De La Cruz-Reyna, A. J. L. Harris, L. P. Flynn, and J.J. Gomez-Palacios, “Infrared satellite monitoring at Popocatepetl: Ex-plosions, exhalations, and cycles of dome growth,” J. Geophys. Res.,vol. 107, 2002, 10.1029/2000JB000125.

[50] Y. Yamaguchi, H. Fujisada, and M. Kudoh, “ASTER instrument char-acterization and operation scenario, in: Calibration and characteriza-tion of satellite sensors,” Adv. Space Res., vol. 23, pp. 1435–1444,1999.

[51] A. Rybin,M. Chibisova, P.Webley, T. Steesen, P. Izbekov, C. Neal, andV. Realmuto, “Satellite and ground observations of the June 2009 erup-tion of Sarychev Peak volcano, Matua Island, Central Kuriles,” Bull.Volcanol., vol. 73, no. 9, pp. 1377–1392, 2011, doi:10.1007/s00445-011-0481-0.

Michael Abrams received degrees in biology andgeology from Caltech.Since 1973 he has worked at NASA’s Jet Propul-

sion Laboratory, in geologic remote sensing. He hasbeen on the science team for many instruments, in-cluding Skylab, HCMM, Landsat, and EO-1. Areasof specialization are mineral exploration, natural haz-ards, volcanology, and instrument validation. He hasbeen on the US/Japan ASTER Science Team since1988, and became the ASTER Science Team Leaderin 2003.

Dave Pieri received the B.S. degree in physicsfrom Villanova University, and the Ph.D. degree ingeology from Cornell University.He joined the JPL staff in 1980. His early interest

in planetary geology led him to the position of ProjectScientist on Viking Mars mission. His ore recent re-search interests are remote sensing of volcanic ashand gas plumes in the context of aviation hazards, andin-situ measurements related to the calibration andvalidation of their remotely sensed properties; appli-cations of robotic vehicles for in-situ observations of

volcanic processes; nature, character, and environmental history of valley net-works on Mars and Titan; and the geomorphology of drainage networks in gen-eral.

Vince Realmuto received the M.S. and Ph.D.degrees in geology from the University of Arizona.Since joining the JPL staff in 1988 he has followed

his interests in application of quantitative remotesensing techniques to the study of volcanic andgeothermal phenomena; application of 3D computergraphics and animation techniques to visualizeearth science data sets; and conceptual design andevaluation of remote sensing instrumentation.

Robert Wright is an Associate Researcher at theUniversity of Hawaii. After receiving the Ph.D.from the Open University, U.K., he pursued hisresearch interest in remote sensing of volcanoes. Hiscurrent projects include hyperspectral temperatureretrievals for active lava flows, domes and lakes;low temperature thermal precursors to large basalticeruptions; operational thermal volcano monitoringusing MODIS (http://modis.higp.hawaii.edu); statis-tical analysis of low spatial resolution satellite datafor volcano surveillance; field-based temperature

measurements of active lava flows; HawaiiView, Satellite Remote Sensing dataand images.