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Database of Crustal Deformation Observed by SAR: ImprovingAtmospheric Delay Mitigation for Satellite SAR Interferometry

and Developing L-Band Multi-Type Portable SAR

Paper:



Taku Ozawa∗1,†, Yosuke Aoki∗2, Satoshi Okuyama∗3, Xiaowen Wang∗4,Yousuke Miyagi∗1, and Akira Nohmi∗5

∗1National Research Institute for Earth Science and Disaster Resilience (NIED)3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan

†Corresponding author, E-mail: [email protected]∗2Earthquake Research Institute, The University of Tokyo, Tokyo, Japan

∗3Meteorological Research Institute, Ibaraki, Japan∗4Southwest Jiaotong University, Sichuan, China

∗5Alouette Technology Inc., Tokyo, Japan[Received January 8, 2019; accepted June 6, 2019]

Spaceborne synthetic aperture radar (SAR) andground-based radar interferometers (GBRIs) can beused to detect spatially detailed crustal deformationsthat are difficult to detect by on-site observations, theGlobal Navigation Satellite System, tiltmeters, and soon. To make such crustal deformation informationreadily available to those engaged in evaluating vol-canic activities and researching the mechanisms, weare preparing a database within the Japan Volcanolog-ical Data Network data sharing system to store crustaldeformation detected by spaceborne SAR and GBRIs(Subtheme 2-1, Project B, the Integrated Program forNext Generation Volcano Research and Human Re-source Development). In this study, we examinedmethods to reduce atmospheric delay noise in SAR in-terferometry using the numerical weather model anddetermined the methods for resampling the analyti-cal values of the numerical weather model and esti-mating atmospheric delay to efficiently determine at-mospheric delay. We show that the atmospheric de-lay can be estimated with higher accuracy by prop-erly combining the isobaric surface and ground sur-face data of the mesoscale model (MSM) providedby the Japan Meteorological Agency. We are devel-oping a multi-type portable SAR system as a GBRIsystem such that it would allow campaign observa-tions whenever increased volcanic activities are ob-served and acquire crustal deformation with a highertemporal resolution than spaceborne SAR for storagein the database. This system employs L-band radar,which has a higher penetrability against vegetation.Two modes of observations are possible: ground-basedSAR and car-borne SAR. The prototype was fabri-cated to conduct experiments necessary to develop aworking model. The experimental observations wascarried out around Asama volcano, and we confirmedthat clear fringe was obtained.

Keywords: volcano, deformation, SAR, GB-SAR, car-borne SAR

1. Introduction

When volcanic activity heightens, it is necessary to ac-curately evaluate the activity status based on observations.In particular, crustal deformation is one of important ob-servation items that allows estimation of the behavior ofmagma and water under the ground. In many volca-noes, crustal deformation has been monitored by obser-vation networks using the global navigation satellite sys-tem (GNSS), tiltmeter and others, and greatly contributesin assessing volcanic activities (e.g., [1, 2]). However,crustal deformations preceding, and following, eruptioncan be spatially and temporally distributed in a complexmanner or occur locally, and the observation density inexisting observation networks may be insufficient to un-derstanding the overall crustal deformation. There mayalso be cases in which significant crustal deformation oc-curs near the crater. Although such crustal deformationis important to evaluate volcanic activities, on-site obser-vations are difficult because it can be dangerous to ap-proach the vicinity of the crater. To resolve those prob-lems, the technology to observe crustal deformation basedon the synthetic aperture radar (SAR) is utilized. SAR isan imaging radar sensor which is mounted on moving ve-hicle such as satellite, and allows the detection of crustaldeformation from the phase difference of correspondingpixels in SAR images observed at two different times.This method is called SAR interferometry, and is used toresearch surface deformations such as in volcanoes, earth-quakes, glaciers, and others (e.g., [3–5]), because it allowsthe areal detection of crustal deformation from a remotesite. This method has been described in many literatures(e.g., [6, 7]), so it will not be described in detail in this pa-

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per. It is now becoming possible to reliably and accuratelydetect the crustal deformation of entire mountains dueto the improved performance of SAR sensor and betteranalysis techniques. Yet, the precise detection of crustaldeformation requires analytical expertise and experience,and is difficult for non-specialists of SAR interferometryanalysis. Furthermore, while it is necessary to refer topast crustal deformation histories to evaluate the volcanoactivities that have become active, considerable time isrequired to carry out such an analysis, and therefore theinformation needed to evaluate volcanic activity is usu-ally not immediately available. These problems can beexpected to be resolved by analyzing SAR data, convert-ing it to crustal deformation, and storing the crustal defor-mation information in a database. Furthermore, the dis-tribution of crustal deformations may change drasticallywithin a short time-span such as a day during periods ofheightened volcanic activities. Although it is important todetect such shifting crustal deformation to assess detailedvolcanic activities, this is difficult with a spaceborne SARbecause the time resolution of the obtained crustal defor-mation is limited by the recurrence time of satellite. Itis expected that this problem can be solved by the use ofground-based radar interferometer (GBRI). GBRI trans-mits a microwave from the ground, and obtains an im-age from analyzing received backscatter wave. ApplyingSAR interferometry to observed GBRI images, it possi-ble to detect crustal deformations with a time resolutionof one hour or shorter. Furthermore, it is possible to ob-serve crustal deformations with a spatial resolution that ishigher than that possible through methods such as GNSS,which require on-site observations, because it allows arealobservation of crustal deformations via spaceborne SAR.A further advantage is that it allows the investigation ofcrustal deformations near a crater when it is difficult toapproach the crater due to high volcanic activity becausethe observations are made by remote sensing technique.

In this study, we employ a spaceborne SAR and GBRIto detect detailed temporal and spatial crustal deforma-tions under Subtheme 2-1 “Developments of portableradar interferometer and spaceborne SAR interferome-try for precise observation of volcano deformation” un-der the Project B “Development of advanced volcanoobservation technology” of the Integrated Program forNext Generation Volcano Research and Human ResourceDevelopment (INeVRH), and create a database of theobtained crustal deformation information under a datasharing system (Japan Volcanological Data Network:JVDN) [8], which is being undertaken under the ProjectA of INeVRH. The creation of such a database for JVDNshould make it easy to compare many types of volcanicobservation data, including seismicity and crustal defor-mations obtained not only by spaceborne SAR and GBRIbut also via the GNSS, tiltmeter, and so on. This willallow their efficient use for the purpose of evaluating vol-canic activities and researching the mechanisms of vol-canic activities. This paper presents the results of our in-vestigation of methods to efficiently reduce atmosphericdelay noise in the spaceborne SAR analysis to produce

a database. In addition, we fabricated a prototype to de-velop a portable GBRI to allow high-mobility observa-tions of crustal deformations during periods of increasedvolcanic activities. We outline the design and present theresults of experimental observations conducted at Asamavolcano.

2. Creation of Database of Crustal Deforma-tion Information Obtained from Space-borne SAR

2.1. Investigation of Standard Analysis Method

By analyzing spaceborne SAR data, converting itto crustal deformation information, and storing it in adatabase, it should become possible for researchers tomake use of crustal deformation information even whenthey have no SAR analysis expertise. If increased vol-canic activities have been observed, crustal deformationhistories can be used rapidly to evaluate them. Thus,the crustal deformation information obtained from space-borne SAR, created under the Project A of INeVRH, isstored in a database within the JVDN [8]. This databasestores the changes in the satellite-ground distance (slantrange) between observations obtained by applying SARinterferometry, along with information such as the in-cident direction vector which is necessary to interpretcrustal deformations. The database will store data on abasic set of 26 volcanoes, made up of the 25 volcanoes [9]designated by the Ministry of Education, Culture, Sports,Science and Technology for selective observation as wellas Hakone volcano. This group will be expanded as nec-essary depending on current volcanic activities and avail-able computing resources. Analyzing SAR data is basi-cally ALOS/PALSAR and ALOS-2/PALSAR-2. Whenemergency observations are conducted by other SARsatellites for investigation of the volcanic activity, theseresults are added as long as the release of such data is per-mitted. The data format and search method of the JVDNare currently being discussed.

It is necessary to analyze a vast amount of SAR datato create the database, and it will therefore be inefficientto carry out the analysis manually, for example visuallychecking the intermediate results to fine-tune the param-eters. Instead, it is necessary to automatically apply SARinterferometry and employ an analysis method that willyield accurate crustal deformation information in a rela-tively stable manner. Therefore, in this study, we investi-gate an analysis method by which accurate crustal defor-mation can be acquired automatically in a relatively sta-ble manner, and construct an analysis system based onthis method. In this investigation, rather than speciallydeveloping a new method, we review standard analysismethods that are generally being employed, and select amethod that is suitable for automatic processing. In ad-dition, we examine a few methods to reduce noise andincorporate them into the analysis for the creation of adatabase. In this paper, this approach will be called the

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Fig. 1. Analysis flow of SAR interferometry analysis fordatabase construction. Two SAR SLC images used in SARinterferometry are accurately co-registered to generate theinterferogram. The intensity image simulated from a digitalellipsoidal height model is aligned with the observed imageto determine the coordinates of image pixels. Based on theresults, the phase components due to topography and orbitaldifferences are simulated, and their phase components areremoved from the interferogram. After reducing the phasecomponents due to atmospheric and ionospheric delays andorbital error, phase unwrapping is applied. The unwrappedresult is converted to geodetic coordinate system data, whichis then combined with the other results to determine the time-series of slant-range change, which is stored in the database.

Standard Analysis Method. To construct the analysis sys-tem, we employ the SAR interferometry analysis toolRINC [5], developed by the National Research Institutefor Earth Science and Disaster Resilience (NIED).

The analysis flow of the Standard Analysis Method,as envisioned at this stage, is shown in Fig. 1. Firstly,the two SAR SLC images used for SAR interferometryare accurately co-registered using affine transformation togenerate the interferogram. The common band filter [10]is applied to SLC images before generation of the inter-ferogram. Secondly, the phase components due to orbitaldifference and topography are simulated using the digi-tal ellipsoidal height model converted into the radar co-ordinate system, and their components are removed frominterferogram. Methods to reduce the atmospheric andionospheric noise components are applied, and finally,the phase component due to orbit error is estimated, af-ter which filtering, unwrapping, and geocoding are ap-plied. Spectral filtering of [11] is used for filtering, whilethe statistical-cost, network-flow phase-unwrapping algo-rithm (SNAPHU) of [12] is used for unwrapping. Subse-quently, the results of other analysis pairs are consideredtogether to estimate the time series of slant-range change,and the results are stored in the database. The time seriesis obtained by a simple analysis, without smoothing in thetemporal and spatial directions.

It is known experientially that most of these processescan be carried out automatically without producing largeerrors. However, the reduction of noises due to atmo-

spheric radar propagation delay (atmospheric delay) andionospheric radar propagation delay (ionospheric delay)is still not in general use. Therefore, in this study, we ex-amine methods to reduce such noises, which we plan toincorporate in the Standard Analysis Method. Althoughunwrapping error is also a large error factor, we plan toselect an analysis procedure that minimizes the numberof unwrapping and relies on the system operator to visu-ally check the error. The method of [13] will be used tocorrect unwrapping error. As we have recently completedour investigation on the method to reduce atmospheric de-lay noise, we discuss the issue in this paper.

2.2. Method to Reduce Atmospheric Delay Noise2.2.1. Atmospheric Delay in SAR Interferometry

When a radar wave propagates through the atmosphere,the radar propagation speed changes and the propagationpath is refracted depending on the refractive index of theatmosphere. This causes the distance between the antennaand ground surface to be estimated longer than the actualdistance. This is called atmospheric delay, denoted byΔρatm and is expressed as the sum of the integration of(n−1) (n: atmosphere’s refractive index) along the prop-agation path and the extension of the propagation path dueto refraction, as follows:

Δρatm =

∫(n−1)ds+(S−G) , . . . . . . (1)

where ds is the length element along the path, and S andG the propagation path length and straight-line distance,respectively, between the satellite and ground surface.The first term on the right-hand side represents the atmo-spheric delay due to changes in the propagation speed,and is called the electrical delay. The second term repre-sents the component of atmospheric delay due to changesin the propagation path, and is called the geometric de-lay. The atmosphere’s refractive index n is obtained bythe following formula [14]:

(n−1)×105 = K1Pd

T+K2

eT+K3

eT 2 , . . . (2)

where Pd is the partial pressure of dry air, e the par-tial pressure of water vapor, and T the temperature. K1,K2, and K3 are coefficients determined experientially, andfound to be K1 = 70.60 (K/hPa), K2 = 70.4 (K/hPa), andK3 = 3.739× 105 (K2/hPa) in [15]. Thus, the electricaldelay can be determined if the three-dimensional struc-ture consisting of the atmospheric temperature and pres-sure, and partial pressure of water vapor is known. Thegeometrical delay can be determined by estimating thepropagation path using the ray tracing method from thethree-dimensional structure of the atmosphere (e.g., [16]).Since SAR interferometry requires measurement of thedifference of two observations, the difference in the at-mospheric delay of two observations is the atmosphericdelay noise in SAR interferometry. Furthermore, the rel-ative difference of atmospheric delay within the image isthe atmospheric delay component appearing in the analy-sis result, because SAR interferometry deals with relative


Ozawa, T. et al.

changes in the image. As the atmospheric delay compo-nent in SAR interferometry can greatly exceed 10 cm de-pending on the conditions, it is essential to reduce it todetect crustal deformation with several centimeters accu-racy.

When we consider the case that the refractive indexis uniform within the atmosphere, the atmospheric delaydisplays a distribution similar to the topography becauseof the difference in the refractive index at the two SAR im-age acquisition times. Oftentimes, this atmospheric cor-relation component is pronounced in the result of SAR in-terferometry, so the atmospheric delay noise can often bereduced to the level of several centimeters by removingthis component. Thus, a method is often used wherebythe topography-correlated component of atmospheric de-lay is approximated by multiplying the elevation by a co-efficient, and this is removed (e.g., [17]). However, thereare cases in which volcanic crustal deformations also cor-relate with the topography, in which case this methodcannot accurately separate the crustal deformation com-ponent and atmospheric delay component. Furthermore,unwrapping is necessary to determine the coefficient, sothat one may not be able to obtain the proper coefficientwhen the large unwrapping error appears. Therefore, itis necessary to carefully check whether the proper coef-ficient is being obtained, which makes this method un-suitable for creating a database by automatically process-ing vast amounts of data. Meanwhile, [18] showed that amethod in which the atmospheric delay is estimated usingthe numerical weather model (mesoscale model: MSM)released by the Japan Meteorological Agency (JMA) isvalid in reducing atmospheric delay noise. This methodhas the advantage that topography-correlated crustal de-formations do not affect estimation of atmospheric delay,and that it does not require unwrapping. Based on a con-sideration of these advantages, we improve the methodproposed in [18] and incorporate it in the Standard Anal-ysis Method.

2.2.2. Estimation of Atmospheric Delay Based on Nu-merical Weather Model

In [18], the propagation path is estimated by ray trac-ing using the MSM isobaric surface data to estimate theelectrical and geometrical delays. However, the methodof [18], when applied in its original form, requires a highcomputational cost. Thus, in this study, we examine waysto increase the efficiency and accuracy of this estimationmethod, and then incorporate it in the Standard Analy-sis Method. In this section, we first describe the methodof [18].

MSM is a numerical weather model used by the JMAto analyze and forecast phenomena such as heavy rain-fall and storms that can cause disasters. Its result is re-leased every 3 hours. The horizontal grid spacing of theprovided MSM data is approximately 10 km for isobaricsurface data and approximately 5 km for ground surfacedata. In the vertical direction, the meteorological param-eters for 16 isobaric surfaces are stored. The computa-tional load in estimating atmospheric delay is high when

Fig. 2. Setting of refractive index distribution and outline ofradar wave refraction for atmospheric delay estimation basedon a numerical weather model. The thick curve representsthe radar propagation path, the solid lines represent the alti-tude surfaces that resample the meteorological data, and thebroken lines represent the center lines between the altitudesurfaces. In the computation for ray tracing, the radar waveis assumed to refract at the midpoints lying on the brokenlines. ni is the refractive index at the i-th layer.

using data that are stored in the isobaric surface data, un-equally geometrical height layer, and therefore it is resam-pled to the geometrically equal interval. For this resam-pling process, the temperature between the isobaric sur-faces is assumed to vary linearly and the pressure to varyexponentially. For humidity, the mixing ratio is assumedto vary linearly with the isobaric surfaces. Since the ge-ometrical height of the uppermost isobaric surface of theMSM (100 hPa) is approximately 16 km, temperaturesand pressures given by U.S. Standard Atmosphere 1976are used for altitudes of 20 km and above. For simplic-ity, the refractive index is assumed to be constant in eachlayer (Fig. 2). Thus, the radar propagates along a straightpath within the layer and refracts in the center of the lay-ers as determined by Snell’s law. When the radar prop-agation path from the image pixel to the altitude of thesatellite is estimated, and its position separates to actualsatellite position one meter or more, the radar incidenceangle at the pixel is corrected and the path is recomputed.Then comparing the estimated radar propagation length tothe straight length between the pixel and satellite position,the geometrical delay is estimated. Meanwhile, the elec-trical delay is estimated along the propagation path andtotal atmospheric delay is estimated from the two images.Even if the estimated satellite position is off its actual po-sition by a meter, this is sufficiently smaller than the gridspacing of the MSM, so the meteorological parameter isalmost the same. Therefore there is little effect on theestimation of the atmospheric delay. The differential at-mospheric delays of the two SAR images used for SARinterferometry are computed for all pixels to produce theimage of atmospheric delay.




2.2.3. Resampling of Meteorological Data

As mentioned in Section 2.2.2, the meteorological datastored in the isobaric surface is resampled to that in thegeometrically equal interval and then used to estimate theatmospheric delay. For determination of resampling lay-ers, the atmospheric delay was integrated in the verticaldirection, and the layers were determined such that theatmospheric delay of each layer would be equal. In thiscalculation, we assumed an atmosphere with the verticaltemperature and pressure profiles given in U.S. StandardAtmosphere 1976, and a humidity of 70% up to 8 kmand zero percent above that. At altitudes of 100 km andhigher, the layer is set every 100 km. When 1 cm was usedas the reference atmospheric delay for a single layer (here-after “intra-layer atmospheric delay”), 249 layers wereobtained. As a test, SAR pairs of southern Kyushu ob-served by ALOS-2/PALSAR-2 on path 131 (ascendingorbit) on July 19, 2016, and March 14, 2017, were an-alyzed. The number of looks for interferogram genera-tion were 14 pixels in the range direction and 15 pixelsin the azimuth direction, and the image size was 1923 ×2487 pixels. When the atmospheric delay of the imagesfor a day was estimated using a personal computer withan Intel Core i7-5500U 2.4 GHz CPU, the analysis tookover an hour. This computational time is excessive be-cause a vast amount of data must be processed to producea database and there are cases requiring immediate anal-ysis. Therefore, we first examined the necessity of tak-ing refraction into account using ray tracing, one of majorfactors in the computational load. In general, the radarrefraction is greater when the incidence angle is larger;however, the radar incidence angle for spaceborne SAR isat most approximately 50◦, where the effect of refractionis not so great. To verify this, we compared the atmo-spheric delays when it was estimated by taking accountof the radar wave refraction and when it is assumed thatthe radar wave propagates along a straight path, using thesame data pair (incidence angle at scene center: approxi-mately 43◦). Of the atmospheric delay of 10 cm obtainedas the result of SAR interferometry of the image pair, thecomponent due to refraction was less than 1 mm. For anincidence angle of this level, the horizontal distance be-tween the radar propagation path and a straight line con-necting the satellite and ground surface is at most severalmeters, and if we assume that this peak occurs at an al-titude of approximately 2 km, the difference in the prop-agation paths is approximately 1 mm. Consequently, ex-tension of the path length due to refraction can be ignoredprovided that we do not consider meteorological condi-tions in which the atmospheric refractive index undergoesdramatic spatial changes. Furthermore, if the radar prop-agation path shifts several meters in the horizontal direc-tion it will cause virtually no change in the meteorologi-cal parameters of the MSM because its grid spacing is ap-proximately 10 km. Therefore, we conclude that the usingray tracing to take account of the refraction of the prop-agation path results in excessive computation. For thisreason, we assume that the radar wave propagates along a

Fig. 3. Relationship of intra-layer atmospheric delay anddifference of estimated atmospheric delay, and computa-tional time required for 1923 × 2487 (pixel) image size.The atmospheric delay for an intra-layer atmospheric delayof 1 cm is used as the reference value. The white circle in-dicates the value adopted in the Standard Analysis Method(intra-layer atmospheric delay: 5 cm).

straight path connecting the satellite and image pixel, andestimate only the electrical delay in the Standard AnalysisMethod. Furthermore, in this case, there is no need to con-sider atmospheric delay at altitudes above 20 km, becausethe meteorological parameter which adopts U.S. StandardAtmosphere 1976 is temporally constant. Therefore, wedetermine the meteorological parameters up to an altitudeof 20 km to estimate the atmospheric delay.

The resampling of the meteorological data to produce249 layers to estimate atmospheric delay constitutes an-other major factor that increases the computational load.Although accuracy is improved by setting the intra-layeratmospheric delay at a low value, which increases thenumber of layers, the computational load is increased.Conversely, the accuracy may be excessively impairedby setting a large intra-layer atmospheric delay to lowerthe computational load. Therefore, to examine the propersetting for the intra-layer atmospheric delay, we investi-gated how a change in the intra-layer atmospheric delayaffects the total atmospheric delay using the same datapair. The results showed that an intra-layer atmosphericdelay of 10 cm produced an atmospheric delay that dif-fered by only approximately 2 mm from that based on1 cm (Fig. 3). This is approximately one-tenth of thecrustal deformation detection accuracy commonly foundin SAR interferometry. Therefore, we adopted its half(5 cm) as the intra-layer atmospheric delay, and used it todetermine the resampled layers. This resulted in 47 layersup to the altitude of 20 km (0, 155, 314, 476, 642, 812,986, 1164, 1347, 1534, 1726, 1923, 2125, 2333, 2546,2766, 2992, 3225, 3465, 3712, 3967, 4230, 4502, 4783,5074, 5376, 5690, 6016, 6355, 6709, 7079, 7466, 7872,8302, 8756, 9236, 9745, 10288, 10870, 11502, 12203,12990, 13888, 14933, 16182, 17735, and 20000 m). Inthis case, less than two minutes were required to computethe atmospheric delay over a day from 1923 × 2487 pixelimages. This time is suitable for analysis to produce adatabase.


Ozawa, T. et al.

2.2.4. Used Meteorological Data

In [18], only the isobaric surface data of MSM wasused; ground surface data was not. Since the atmo-spheric delay is generally greater at lower altitude, how-ever, the estimation accuracy of atmospheric delay mayimprove by additionally using the ground surface datawhich contains detailed meteorological parameters nearthe ground. Furthermore, in [18], the meteorological pa-rameters at the time of SAR image acquisition were de-termined by linearly interpolating the three-hourly MSManalytical values. The estimation accuracy of atmosphericdelay may also be improved by obtaining meteorologi-cal data at shorter intervals. To examine this possibil-ity, we combined the MSM isobaric surface and groundsurface data using the Weather Research and Forecast-ing Model (WRF) [19] developed as a joint project of theNational Center for Atmospheric Research (NCAR) andUnited States National Centers for Environmental Predic-tion (NCEP), to obtain hourly MSM analytical values forthe same spatial grid as the MSM, and used those valuesin an attempt to reduce atmospheric delay noise. In thisinvestigation, we analyzed ALOS-2/PALSAR-2 data of28 scenes acquired between February 2015 and Septem-ber 2017. They were acquired from descending orbit(path 23) by the right-looking mode, and the radar inci-dence angle around the center of the Kirishima volcanogroup was approximately 35◦. Observation mode wasSM1 (bandwidth: 80 MHz), and the radar polarizationwas HH. Furthermore, the change in slant range in inter-ferometric pairs with data acquisition intervals of one yearor less was used to estimate their time series. The targetarea in this analysis is the Kirishima volcano group and itsvicinity, located in Kyushu, Japan (Fig. 4(a)). Shinmoe-dake in the Kirishima volcano group erupted in October2017, and data acquired up to the eruption occurrencewas used in this analysis. According to GNSS observa-tion by V-net, the baseline extension between KRMV andKRHV sites had been observed intermittently until 2016from 2011, and after that, the continuous baseline exten-sion had observed until the 2018 eruption (Fig. 4(b)). Thebaseline extension of approximately 2 cm was observedduring the period of the present SAR analysis. If we as-sume that this extension is similar to that observed aroundthe 2011 eruption, it can be considered to be caused by theinflation of the magma chamber located approximately7.5 km below KRMV [2], and indicates increased vol-canic activities. Furthermore, if a source of crustal de-formation located at a relatively shallow depth close toShinmoe-dake, which erupted in 2017 exists, spaceborneSAR analysis may reveal crustal deformations that wentunobserved in the earlier observations.

Figure 5(a) shows the time-series of slant range changeestimated from results of SAR interferometry without at-mospheric delay reduction. Slant range changes in excessof 10 cm can be seen in mountainous areas, mainly insummer. While GNSS observations show a tendency toexpand during the period subjected to analysis (Fig. 4(b)),such deformation is not seen in it. Furthermore, no

changes in excess of 10 cm are obtained from GNSS.Based on these considerations, we conclude that this largeslant range change in Fig. 5(a) consists largely of non-crustal deformation components caused by atmosphericdelay and other noises. Fig. 5(b) shows the time seriesof slant range changes when only the MSM isobaric sur-face data was used to reduce atmospheric delay noises.The slant range change is smaller than in the results with-out atmospheric noise reduction. Although slant rangechanges in excess of 5 cm can be seen in a few instanceson the western flank of Kirishima volcano group, suchcrustal deformation went unobserved by GNSS, indicat-ing that the reduction of atmospheric delay noise was in-sufficient. Fig. 5(c) shows the results when analyticalvalues obtained with WRF were used to reduce atmo-spheric delay noises. The slant range change is smallerthan in the results obtained by using MSM isobaric sur-face data. Then, the rate of slant range change was es-timated from the estimated time-series, and the standarddeviations of the residuals were calculated (Fig. 6). Thestandard deviation is greater in areas with a high altitudewhen atmospheric noise reduction was not applied, witha maximum of 5 cm. The standard deviation is smaller inthe results applied the atmospheric noise reduction usingMSM isobaric surface data. A maximum standard devia-tion of approximately 2 cm is found on the western flankof Kirishima volcano group, but this is further reduced inthe analysis using WRF values. The mean standard devi-ations over the entire analyzed area were 16 mm, 11 mm,and 10 mm for the cases when no method was used to re-duce atmospheric delay, when MSM was used, and whenWRF was used, respectively. Thus, the WRF-based anal-ysis produced the smallest standard deviation. To exam-ine the atmospheric delay component correlated to topog-raphy, the slant range changes for the respective periodswere fitted with the following formula:

Δρ = Ah+Bxy+Cx+Dy+E, . . . . . . (3)

where Δρ denotes the obtained slant range change, h theellipsoidal height of the pixel, x and y the image pixelpositions, and A, B, C, D, and E, coefficients. Coeffi-cient A represents the topography-correlated component;a smaller value indicates that the atmospheric delay noiseis reduced better. The average magnitude of coefficientA was 3.2 × 10−5 m/m for the results in which the at-mospheric delay noise reduction was not applied, but itwas reduced to 1.8 × 10−5 m/m for the results in whichthe reduction of the atmospheric delay noise with MSManalytical values was applied. It was 1.5 × 10−5 m/mwhen the reduction of atmospheric delay noise was ap-plied with WRF analytical values, displaying further im-provement. This is equivalent to approximately 2 cm ofthe atmospheric delay at the altitude of 1500 m, which isroughly consistent with the top of Shinmoe-dake.

These results indicate that the atmospheric delay is re-duced more accurately when WRF analytical values wereused, compared to when only MSM isobaric surface datawas used. However, the use of meteorological parame-ters at the time of SAR data acquisition, obtained by lin-




Fig. 4. (a) Target area for test analysis to consider the method of the atmospheric noise reduction. Blue circles indicate V-netobservation sites. (b) Temporal change of baseline length between KRMV and KRHV sites observed by GNSS. Arrows at thetop indicate times of occurrence of the 2011 Shinmoe-dake eruption, the 2016 Kumamoto earthquakes, the 2017 Shinmoe-dakeeruption, and the 2018 Shinmoe-dake eruption.

Fig. 5. Results of test analysis to consider the method of the atmospheric noise reduction. For this analysis, PALSAR-2 dataacquired from ALOS-2 path 23 (descending orbit, right-looking) were used. The images show slant range changes from February9, 2015, to the observation dates indicated above the images. The image area corresponds to Fig. 4(a). (a) Results obtained withoutapplication of methods to reduce atmospheric delay. (b) Results obtained when only MSM isobaric surface data was used to reduceatmospheric delay. (c) Results obtained when WRF analytical values, based on MSM isobaric surface and ground surface data,were used to reduce atmospheric delay.

ear interpolating the three-hourly WRF analytical values,did not produce a noticeable difference. Although we ex-pected that the use of meteorological data sampled at afiner time resolution using WRF would improve the accu-racy, the effect was minor, as indicated by this observa-tion. Thus, we speculate that the improvement from usingWRF analytical values was mainly derived from the com-bined use of the MSM isobaric surface and ground surfacedata using WRF.

2.3. Crustal Deformations Around Shinmoe-Dakeand Iwo-Yama

According to the results presented in Section 2.2.4,significant crustal deformations were detected aroundShinmoe-dake and Iwo-yama (Fig. 6(c)). In this section,we briefly outline these crustal deformations.

The complex distribution of the slant range change wasobtained in the Shinmoe-dake crater (Fig. 7(a)). Specifi-cally, they are a slant range contraction in excess of 10 cm


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Fig. 6. (a)–(c) Slant range change rate estimated from the time-series (Fig. 5). (d)–(f) Root-mean-square of residuals from theestimated linear trend. (a) and (d) are the results when no method was used to reduce atmospheric delay, (b) and (e) those whenonly MSM isobaric surface data was used to reduce atmospheric delay, and (c) and (f) those when WRF analytical values based onMSM isobaric surface and ground surface data were used to reduce atmospheric delay. The image area corresponds to Fig. 4(a).

Fig. 7. Time-series of slant range change obtained in test analysis. (a) Enlarged images of Shinmoe-dake. (b) Enlarged images ofIwo-yama. All images show the slant range change between February 9, 2015, and the respective dates given above the images.




in two and a half years and a slant range extension inits east area. The slant range contraction area were sur-rounded by the donut-shaped incoherent region. It isthought that the incoherent region on the margins of theuplifted region was produced due to the steep spatial gra-dient of the deformation, which resulted in reduced coher-ence, and thus was masked during the unwrapping pro-cess. With regard to the slant range contraction, [2] hasfound that a gradual local uplift has occurred continu-ally after cessation of the 2011 Shinmoe-dake eruption,of which the above observation appears to be a continu-ation. This uplift appears to have mostly ceased by theend of 2016. The area lying slightly to the east displaysslant range extension suggesting subsidence from around2016, but since its location differs from the uplift area, itappears that the two were not caused by the increase anddecrease of the same source. In the outside of the crater, acontraction of slant range can be observed centered at theeast side of the crater, suggesting inflation. This occursaround May and June of 2016, then seems to settle forsome time, but then rapidly begins to increase from thesummer of 2017. Shinmoe-dake erupted in October 2017,and this deformation may have been a precursor. We willinvestigate this in more detail in a future study.

Around Iwo-yama, a significant contraction of slantrange, suggesting inflation, can be observed for a 500 m-diameter area (Fig. 7(b)). This deformation begins tooccur around fall of 2015, and the uplift appears to in-crease in magnitude intermittently. Although no data ex-ists for the preceding several years, and we therefore can-not see deformation during that period, slant range exten-sion were observed in the area in the 1990s from SARinterferometry using JERS-1 SAR data [20]. Iwo-yamaunderwent a small-scale phreatic eruption in March 2018,so this uplift may have been a precursory movement.

From this analysis we were able to automatically de-tect crustal deformations of several centimeters associatedwith volcanic activities. This verifies the usefulness of thecrustal deformation information obtained in this analysis.In the future, we will also investigate the method to reduceionospheric delay noise to further improve accuracy.

3. Multi-Type Portable SAR

3.1. Development Background

During periods of increased volcanic activity, there arecases in which the magnitude or spatial distribution ofcrustal deformation changes drastically over a short timeperiod. Such crustal deformations are important data inassessments of the immediate possibility of volcanic erup-tions or understanding the mechanism of eruption. In thisstudy, we wish to observe such crustal deformations andapply the findings in the evaluation of volcanic activi-ties or research on the mechanisms of volcanic activities.However, the observation frequency of spaceborne SARis limited by the recurrence time of the satellite, whichmakes it difficult to observe crustal deformations under-

going rapid change by spaceborne SAR. In recent years,research on application of GBRI, including ground-basedSAR (GB-SAR) (e.g., [21]) and a portable real apertureradar interferometer [22] to detect surface deformationhas progressed. Since GBRI allows observation of sur-face deformation at a high temporal resolution, there arehigh expectations that it can be used to observe dras-tic crustal deformations accompanying volcanic activitiesthat are difficult to observe with spaceborne SAR (e.g.,[23]). Thus, we wish to set up a GBRI-based observationsystem that can be deployed with high mobility when in-creased volcanic activities have been observed. However,the GBRI systems currently in wide use mainly employradar wave in the 17 GHz (Ku-band), which has a lowpenetration against vegetation, and thus are unsuitable formonitoring the surface deformation in terrain other thanbare land. The areas surrounding volcanoes are often cov-ered by dense vegetation, and there is a need for a GBRIsystem that can be used to observe crustal deformations ofvegetated terrain to observe crustal deformations that ac-company volcanic activities. Therefore, we conducted alaboratory experiment to measure the penetration againstvegetation [24] and verified that radar wave in the 1 GHz(L-band) are suitable for this purpose. Based on this re-sult, we decided to develop a GBRI system that employsL-band radar.

Generally, GBRI systems are used fixing at a single siteto carry out observation. However, GBRI, which trans-mits radar waves from the ground, cannot observe theentire volcano from single point because radar waves donot reach many areas due to terrain shielding. Althoughthis problem can be solved by making simultaneous ob-servations from several sites around the volcano targetedfor observation, this is not easy to carry out. There-fore, we intend to develop a GBRI system that makesit possible to efficiently carry out repeated observations,so that the crustal deformations over an extensive areacan be observed with a time resolution of approximatelyone day. It is possible to make repeated observationsfrom multiple points using the conventional method (GB-SAR), in which an observation is made by moving the an-tenna along rails set on the ground. If the system can bemounted on an automobile (car-borne SAR) and can carryout SAR observation by its moving, this should enable ef-ficient multiple observations within a short time period. Insuch a car-borne SAR system, processing must take intoaccount the non-linear travel trajectory of the antenna,speed variations, and vibrations, and accurately compen-sate for the effects of obstructions. Although the accuracyof crustal deformation detection may be lower than con-ventional GB-SAR, especially if the compensatory mea-sures are inadequate, its advantage lies in making possibleobservations of an extensive area in a short time. In thisstudy, we therefore develop a SAR system that allows theuse of either GB-SAR or car-borne SAR, depending onthe circumstances (called “multi-type portable SAR” inthis paper). At this stage, we have produced its prototype.In this chapter, we describe the prototype of the multi-typeportable SAR.


Ozawa, T. et al.

Fig. 8. Photo of the prototype of the multi-type portable SAR.

3.2. System SpecificationsBased on the results of the laboratory experiment on

radar vegetation penetrability [24], we decided to em-ploy L-band radar, which is less affected by vegeta-tion, for the multi-type portable SAR, with a 1.30–1.37 GHz frequency (bandwidth 70 MHz). The centralfrequency and bandwidth for observation can be modi-fied within this range to suit the observation conditions.Linear frequency-modulated continuous wave (FMCW)was used as the modulation method of the transmittedradar waves, because it has a relatively high signal-to-noise ratio, even at low power transmission. The rep-etition frequency used for frequency modulation can bemodified depending on the observation conditions. It wasfound that horizontal polarization has a higher penetra-tion against vegetation as compared to vertical polariza-tion [24], so horizontal polarization is employed for thetransmitted/received radar wave. We consider the antennapower to 100 mW.

The slant range resolution of the images acquired byFMCW radar can be expressed by 1.3c/(2BW ), whereBW is the transmission frequency bandwidth and c thespeed of light. 1.3 represents the resolution drop due tothe window function. The slant range resolution is 2.8 mwhen measured at the maximum bandwidth of 70 MHz.The azimuth resolution can be expressed as 0.89λ R/2D,where λ is the radar wavelength, D is the synthetic aper-ture length, and R is the slant range. When a 10 m rail isused, the azimuth resolution for an observation distance of4 km is approximately 40 m. Thus, it is expected that theradar interferometer with these specifications will have aspatial resolution satisfactory for assessing the volcaniccrustal deformations.

Figure 8 shows a photo of the prototype of the multi-type portable SAR. It has the following system configura-tion: The unit consists of a head unit and rail unit (Fig. 9).The head unit consists of the transmit/receive unit, anten-nas, GNSS/IMU, motor control unit, and battery. Thisunit is moved along the rails for observation in the GB-SAR mode, and is mounted on a car for observation in thecar-borne SAR mode. The transmit/receive unit consistsof the control PC (Windows OS), signal processing unit,

Fig. 9. System configuration of the prototype of the multi-type portable SAR.

transmitter, receiver, and power source. The control PCis connected to an external control PC via Wi-Fi or cableLAN and controlled using the remote desktop software,although it can be connected to a monitor display or inputdevice if the operator wishes to do so. The signal process-ing unit generates the reference clock for signal transmis-sion, digitally converts received signals and formats themfor output, and automatically adjusts the signal receptionlevel (AGC: automatic gain control). The transmitter con-sists of the waveform generator, which generates the L-band signals for transmission, and power amplifier whichamplifies the signals to the required power. The antennaunit consists of two planar patch antennas: The transmit-ting and receiving antennas. The antenna elevation an-gles can be varied in the range −45 to +45◦ depending onthe observation conditions. The GNSS/IMU unit housesSPAN-CPT (NovAtel Inc.), and outputs the position, atti-tude, velocity, and time, as digital data. It also outputs a1PPS time calibration signal to the transmit/receive unit.




Fig. 10. Results of performance evaluation experiment for the prototype of the multi-type portable SAR. (a) Antenna pattern inazimuth direction of transmitting antenna. Colors indicating frequencies are shown in the legend below the figure. (b) Antennapattern in elevation direction of transmitting antenna. Colors indicating frequencies are shown in the legend below the figure.(c) Frequency of received radar wave plotted against time, when measured at settings of 70 MHz bandwidth and 500 μsec pulserepetition frequency. Abscissa scale division is 100 μsec. (d) Phase change (radian) during 2-hour continuous measurement.

The motor control unit consists of the drive motor, motordriver, and power source for the motor. The power sup-plied by the battery is delivered via the gate of the motorcontrol unit to the transmit/receive unit. Furthermore, the12 V battery also supplies the 24 V for application to themotor. A general car battery is used because it can bepurchased in areas close to volcanoes.

The rails, which are used for stationary ground-basedobservation, have a maximum length of 10 m, whichyields a 40 m azimuth resolution for an observational dis-tance of 4 km. They were made as an assembly-type, inwhich 2 m rail units are connected, because 10 m railswould be difficult to transport. The length can be ad-justed to suit observational conditions. The rails haveracks which mesh with the gears attached to the bottomof the head unit, and the head unit moves along the railswhen the gears are turned. Position of the head unit canbe accurately obtained from the encoder of the gear.

3.3. Experiment to Verify PerformanceWe carried out measurements at the radio anechoic

room of the Electronic Navigation Research Institute toverify whether the prototype of the multi-type portableSAR unit performs as per the specifications. Measuredantenna pattern for the transmission antenna is shown inFigs. 10(a) and (b). The antenna −3 dBi beam width forthe center frequency (1.335 GHz) was determined to be32 degrees in the azimuth direction and 62–64◦ in the

elevation direction. Almost same antenna pattern wasobtained for the receive antenna. We then carried outmeasurements on the transmission performance of theradar waves. For this measurement, a corner reflectorwas placed at a distance of 22 m in front of the radarantenna, and the reflecting waves were measured to de-termine the radar’s pulse repetition frequency, frequencyband, received power, and phase stability. The measure-ment took place at a very close range, so it was conductedby connecting a 20 dB attenuator. Fig. 10(c) shows thefrequency change of the received radar waves over time,from which it can be seen that the radar was transmitted atthe specified pulse repetition frequency. It also confirmsthat the transmitted radar waves were properly linearlymodulated. The antenna power was found to be 88.5 mW,which is within specifications. The radar interferometrymust contain no phase distortion and display long-termstability to obtain accurate measurements of crustal defor-mations. We thus conducted a two-hour continuous mea-surement and examined the phase changes in the reflectedwaves from the corner reflector, and a phase change wasless than 0.2◦ (equivalent to a deformation of less than0.1 mm) (Fig. 10(d)). Variations of this level can be ig-nored, so the phase stability of the prototype is sufficientlyaccurate for crustal deformation measurements.

We conducted field observations targeting Asama vol-cano on December 13, 2018. Asama volcano was cov-ered with snow at the time. Fig. 11(a) shows a photo cap-


Ozawa, T. et al.

Fig. 11. Measurement results of field experiment at Asama volcano. (a) Asama volcano viewed from the observation site. Orangebroken curve indicates the southeastern rim of the Onioshidashi lava flow. (b) Intensity image. White broken curve indicates theapproximate caldera rim. Orange broken curve indicates the southeastern rim of the Onioshidashi lava flow. (c) SAR interferogramobtained from interferometric pair of 12:41–12:51 (JST). (d) SAR interferogram obtained from interferometric pair of 12:41–13:01 (JST). (e) SAR interferogram obtained from interferometric pair of 12:41–13:11 (JST). (f) Coherence image obtained frominterferometric pair of 12:41–12:51 (JST). (g) Coherence image obtained from interferometric pair of 12:41–13:01. (h) Coherenceimage obtained from interferometric pair of 12:41-13:11 (JST). Yellow curves in (b)–(h) indicate range from the observation site.

tured from the observation point. For this observation, tenmeasurements were carried out during an approximately30-minute period. Although we are currently still inves-tigating analysis methods and the results presented hereare preliminary results, we were able to capture scatteringfrom the vicinity of the peak, which lies 4 km from the

observation point (Fig. 11(b)). Our objective in develop-ing the sensor is to enable observation of the crater and itsvicinity even when access within a 4 km zone is restrictedand therefore the preliminary results indicate that this goalhas been met. However, further investigation to improvethe signal-to-noise ratio, sensitivity is necessary for ob-




taining clearer image. The interferograms show relativelyminor degradation of coherence for observations of ap-proximately 30 minutes, displaying coherence of nearly1.0 (Figs. 11(c)–(h)). Although snow cover can some-times cause degradation of coherence, the weather con-ditions were such that there was very little melting of thesnow during the observation period, which is why the pix-els display very little scattering change and coherence didnot degrade significantly. A high coherence is obtainedat the edges of the images, which are thought to be ar-eas of radar shadows. This may be due to interferenceby the noise components, but this will require a more de-tailed investigation in the future study. A phase changeof approximately 2 radian during 30-minute was obtainedin the vicinity of the 3 km range. We think that this isnot due to crustal deformation but atmospheric delay, be-cause the activity of Asama volcano has been inactive atthis time. In the future study, we attempt to reduce thisby combining estimations of the atmospheric delay basedon the numerical weather model and time-series analysis,similar to the spaceborne SAR analysis. Furthermore, wewill develop hardware and software that will enable effi-cient repeated and car-borne observations.

4. Summary

In this study, we employ spaceborne SAR and multi-type portable SAR to detect high spatial-resolution crustaldeformations that are difficult to obtain from observa-tion networks based on GNSS or tiltmeters, and createa database of the acquired crustal deformations. For thispurpose, we investigated a method to analyze spaceborneSAR (Standard Analysis Method), and are currently de-veloping the multi-type portable SAR. In the StandardAnalysis Method, we examined the method proposedin [18] to reduce atmospheric delay noise based on anumerical weather model. We were able to reduce thecomputational load without substantial reduction of accu-racy by assuming a straight-line radar propagation pathbetween the satellite and pixel and by resampling mete-orological data. Furthermore, we were able to improveaccuracy using analytical values obtained by merging theMSM isobaric surface data and ground surface data us-ing WRF. We were also able to detect crustal deforma-tions accompanying volcanic activities at Shinmoe-dakeand Iwo-yama by employing the proposed method of au-tomatic analysis and thus demonstrated the usefulness ofthe database. Further to this study, we will investigatemethods to reduce the ionospheric delay noises and meth-ods for producing time-series data in the future study.To create the database, we cooperate with the SAR Re-search Group on crustal deformations in Japan (PIXEL:PALSAR Interferometry Consortium to Study our Evolv-ing Land surface) and use data shared by PIXEL. To makeefficient use of data, we are also constructing a data serverfor collecting SAR data. We are conducting SAR analy-sis for future database creation, and in some volcanoes,interesting crustal deformations associated with volcanic

activity have been detected [25]. About developing themulti-type portable SAR, we have fabricated the proto-type and confirmed that it possesses the performance nec-essary to conduct experiments. In the future, we will con-duct experimental observations, and will determine thesystem specifications needed to carry out high-accuracyand efficient observations by ground-based and car-borneSAR systems. Then we will produce an operational unitbased on these specifications. After that, we will makeobservations when increased volcanic activities have beenreported, and store the obtained crustal deformation infor-mation in the database. We are considering the observa-tion of all land volcanoes in Japan that are permitted emis-sion of radar wave by the Minister of Internal Affairs andCommunications under the Radio Act.

AcknowledgementsThe authors would like to thank two anonymous reviewers whoread the manuscript carefully and provide highly constructivecomments for revision of manuscripts. A part of this study isconducted under Subtheme 2-1, Project B of INeVRH, and an-other part is conducted under the SAR Research Group (PIXEL)based on Specific Research Program (B) (2018-B-02), Earth-quake Research Institute (ERI), the University of Tokyo. ThePALSAR-2 data used in this study were provided under a jointERI and Japan Aerospace Exploration Agency (JAXA) researchprogram and was shared by PIXEL. The original PALSAR-2 dataare owned by JAXA. In the SAR analysis and image creation, weused GEONET F3 solution and 10 m-mesh digital elevation mod-els of the Geospatial Information Authority of Japan (GSI). Theanalytical solutions of the Meso-Scale Model (MSM) providedfrom Japan Meteorological Agency (JMA) were used in this study.We used the Generic Mapping Tools [26] for drawing the figures.

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[25] X. Wang and Y. Aoki, “Post-eruptive thermoelastic deflation of in-truded magma in Usu volcano, Japan, 1992-2017,” J. Geophys. Res.Solid Earth, doi: 10.1029/2018JB016729, 2019.

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Name:Taku Ozawa

Affiliation:Chief Researcher, National Research Insti-tute for Earth Science and Disaster Resilience(NIED)

Address:3-1 Tennodai, Tsukuba, Ibaraki 305-0006, JapanBrief Career:2000-2001 COE Researcher, National Institute of Polar Research2002-2004 JST Domestic Research Fellowship (Research in GSI)2004- Researcher, NIEDSelected Publications:• T. Ozawa, E. Fujita, and H. Ueda, “Crustal deformation associated withthe 2016 Kumamoto Earthquake and its effect on the magma system ofAso volcano,” Earth Planets Space, Vol.68, doi:10.1186/s40623-016-0563-5, 2016.• T. Ozawa and T. Kozono, “Temporal variation of the Shinmoe-dakecrater in the 2011 eruption revealed by spaceborne SAR observations,”Earth Planets Space, Vol.65, pp. 527-537, 2013.• T. Ozawa and E. Fujita, “Local deformations around volcanoesassociated with the 2011 off the Pacific coast of Tohoku Earthquake,” J.Geophys. Res., Vol.118, pp. 390-405, doi: 10.1029/2011JB009129, 2013.Academic Societies & Scientific Organizations:• Geodetic Society of Japan• Volcanological Society of Japan (VSJ)• American Geophysical Union (AGU)• Japan Geoscience Union (JpGU)• Seismological Society of Japan (SSJ)

Name:Yosuke Aoki

Affiliation:Earthquake Research Institute, The University ofTokyo

Address:1-1-1 Yayoi, Bunkyo, Tokyo 113-0032, JapanBrief Career:2001 Ph.D., The University of Tokyo2001-2003 Lamont Postdoctoral Fellow, Lamont-Doherty EarthObservatory of Columbia University2003- Research Associate, Assistant Professor, and Associate Professor,Earthquake Research Institute, The University of TokyoSelected Publications:• Y. Aoki, K. Tsunematsu, and M. Yoshimoto, “Recent progress ofgeophysical and geological studies of Mt. Fuji Volcano, Japan,”Earth-Science Reviews, Vol.194, pp. 264-282, doi:10.1016/j.earscirev.2019.05.003, 2019.• Y. Aoki, “Space geodetic tools provide early warnings for earthquakesand volcanic eruptions,” J. of Geophysical Research Solid Earth, Vol.122,pp. 3241-3244, doi: 10.1002/2017JB014287, 2017.• Y. Aoki, “Monitoring temporal changes of seismic properties,” Frontiersin Earth Science, doi: 10.3389/feart.2015.00042, 2015.Academic Societies & Scientific Organizations:• American Geophysical Union (AGU)• Institute of Electrical and Electronics Engineers (IEEE)




Name:Satoshi Okuyama

Affiliation:Researcher, Meteorological Research InstituteVolcanology Research Department

Address:1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JapanBrief Career:2007- National Institute of Advanced Industrial Science and Technology2009- Hokkaido University2014- Japan Atomic Energy Agency2015- Meteorological Research InstituteSelected Publications:• “Correction of Unwrapping Errors Caused by Branch-Cut Algorithm,” J.of the Geodetic Society of Japan, Vol.56, pp. 149-153, 2010 (in Japanese).Academic Societies & Scientific Organizations:• Geodetic Society of Japan• Volcanological Society of Japan (VSJ)

Name:Xiaowen Wang

Affiliation:Professor, Faculty of Geosciences and Environ-mental Engineering, Southwest Jiaotong Univer-sity

Address:No.111, North 1st Section, 2nd Ring Road, Chengdu, Sichuan, ChinaBrief Career:2015-2016 Research Assistant, The Chinese University of Hong Kong2017-2019 Postdoctoral Researcher, The University of Tokyo2019- Associate Professor, Southwest Jiaotong UniversitySelected Publications:• “Post-eruptive thermoelastic deflation of intruded magma in Usuvolcano, Japan, 1992-2017,” J. of Geophysical Research: Solid Earth,Vol.124, pp. 335-357, 2019.• “3D coseismic deformations and source parameters of the 2010 Yushuearthquake (China) inferred from DInSAR and multiple-aperture InSARmeasurements,” Remote Sensing of Environment, Vol.152, pp. 174-189,2014.Academic Societies & Scientific Organizations:• American Geophysical Union (AGU)

Name:Yousuke Miyagi

Affiliation:Chief Researcher, National Research Insti-tute for Earth Science and Disaster Resilience(NIED)

Address:3-1 Tennodai, Tsukuba, Ibaraki 305-0006, JapanBrief Career:2007-2011 Researcher, Japan Aerospace Exploration Agency (JAXA)2012- Researcher, NIEDSelected Publications:• Y. Miyagi, T. Ozawa, T. Kozono, and M. Shimada, “Long-term lavaextrusion after the 2011 Shinmoe-dake eruption detected by DInSARobservations,” Geophysical Research Letter, Vol.41, No.16, doi:10.1002/2014GL060829, 2014.• Y. Miyagi, T. Ozawa, and M. Shimada, “Crustal deformation associatedwith an M8.1 earthquake in the Solomon Islands, detected byALOS/PALSAR,” Earth and Planetary Science Letters, Vol.287,pp. 385-391, 2009.• Y. Miyagi, J. T. Freymueller, F. Kimata, T. Sato, and D. Mann, “Surfacedeformation caused by shallow magmatic activity at Okmok volcano,Alaska, detected by GPS campaigns 2000-2002,” Earth Planets Space,Vol.56, pp. e29-e32, 2004.Academic Societies & Scientific Organizations:• Volcanological Society of Japan (VSJ)• Geodetic Society of Japan• Seismological Society of Japan (SSJ)

Name:Akira Nohmi

Affiliation:Managing Director, Alouette Technology Inc.

Address:3-2-24 Shimorenjaku, Mitaka, Tokyo 181-0013, JapanBrief Career:2006 BSBA, Northeastern University2007- Alouette Technology Inc.


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