Matsuoka, M. and Estrada, M.

Paper:Development of Earthquake-Induced Building Damage
Estimation Model Based on ALOS/PALSAR Observing
the 2007 Peru Earthquake
Masashi Matsuoka∗ and Miguel Estrada∗∗∗ Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

Nagatsuta 4259-G3-2, Midori-ku, Yokohama 226-8502, Japan
E-mail: matsuoka.m.ab@m.titech.ac.jp∗∗ Japan-Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID), National University of Engineering
Av. Túpac Amaru 1150, Lima 25, Peru
E-mail: estrada@uni.edu.pe[Received November 11, 2012; accepted December 14, 2012]

With the aim of developing a model for estimatingbuilding damage from synthetic aperture radar (SAR)
data in the L-band, which is appropriate for Peru, we
propose a regression discriminant function based onfield survey data in Pisco, which was seriously damaged in the 2007 Peru earthquake. The proposed
function discriminates among damage ranks corresponding to the severe damage ratio of buildings using
ALOS/PALSAR imagery of the disaster area before
and after the earthquake. By calculating differences inand correlations of backscattering coefficients, whichwere explanatory variables of the regression discriminant function, we determined an optimum window
size capable of estimating the degree of damage moreaccurately. A normalized likelihood function for thesevere damage ratio was developed based on discriminant scores of the regression discriminant function.
The distribution of the severe damage ratio was accurately estimated, furthermore, from PALSAR imagery
using data integration of the likelihood function withfragility functions in terms of the seismic intensity ofthe earthquake.
Keywords: severe damage ratio, ALOS/PALSAR, the2007 Peru earthquake, likelihood function, backscattering
coefficient, data integration

1. Introduction
Satellite remote sensing is being increasingly used forquick assessment of the impact of natural disasters occurring worldwide [1]. The size and location of affectedareas are estimated, for example, by comparing post- withpre-event imagery, since satellites orbiting the earth often
observe pre-event states at many locations [2, 3]. In recent
years, the resolution of optical sensors on board satellites
has increased to approximately 60-cm ground resolution,
1. This paper is translated with revision from the paper published in the
Journal of Japan Association for Earthquake Engineering, Vol.12, No.6,
pp. 36-49 in Japanese.

346

which allows us to visually interpret the shapes of buildings and the states of large-scale damage [4, 5]. If a disaster strikes an extensive area, however, it takes considerable time to assess damage through visual interpretation,
making it impractical for rapid assessment. In addition,
because damage estimation by visual interpretation inherently involves variability attributable to differences in theperception of the interpreter, there are problems with standardization and the general versatility of the method. Inorder to overcome such disadvantages, attempts have involved the use of a network of many volunteers to visuallyinterpret building damage in an extensive disaster area,
namely, crowdsourcing [6]. There is a need to improve
the accuracy of the visual interpretation of damage. To
this end, engineers with knowledge and experience in interpreting aerial imagery should prepare instruction manuals that allow nonexperts to participate in this visual interpretation of damage, and should also establish a screening technique to eliminate unreliable data. Such a systemof exploiting the enormous human resources outside thedisaster area is expected to become a technology used in
responding to major disasters.
Synthetic aperture radar (SAR), a type of “remote sensing sensor,” observes the surface of the earth day andnight, regardless of weather. If the visual interpretation of damage from SAR imagery is practicable, it willcomplement visual interpretation from optical sensor imagery. Unlike optical sensor images, which look like
photographs, however, SAR imagery represents the intensity of microwave backscattering from the ground surface and is unfamiliar to nonexperts. It is therefore difficult to visually interpret damage from SAR images bycrowdsourcing. It is thus expected that damage from
SAR imagery will be extracted by computer-based image
processing [7–10]. Estimation models for building damage ratios have therefore been proposed [11–13] basedon C-band (wavelength: 5.7 cm) SAR imagery in whichbuilding damage data obtained from detailed field surveys
conducted after the Kobe earthquake in 1995 were used
as ground-truth data and the applicability of one of themodels used for damage extraction in other earthquakes
Journal of Disaster Research Vol.8 No.2, 2013

Development of Earthquake-Induced Building Damage Estimation Model
Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

Fig. 1. PALSAR imagery obtained before and after the 2007 Peru earthquake: (a) July 12, 2007; (b) August 27, 2007.

occurring in various countries and regions was investigated [12]. The model was further improved so that it wasalso applicable to imagery obtained by JERS-1/SAR and
ALOS/PALSAR (PALSAR imagery), which is L-band(wavelength: 23 cm) SAR mounted on Japanese satellites. It was then applied to PALSAR imagery obtained in
the 2007 Peru earthquake and the 2008 Wenchuan, China,
earthquake [13]. It was found from comparison with damage assessment reports of these earthquakes, etc., that
local damage areas could not be detected because themodel was based on the ground-truth data of the 1995
Kobe earthquake. In other words, it was demonstrated
that application of the model to other countries and regions, which have urban structures, building types, anddamage situations different from Japan, gave inaccurate
results [13].
Following procedures reported in our previous papers [11, 13], this paper develops a model for estimating
a severe damage ratio of buildings that reflects the building types and damage situation in Peru. The model is an
estimation model for severe damage ratio optimized for
Peru because it is based on PALSAR imagery of Pisco
after the 2007 Peru earthquake and on building damage
data obtained from field surveys. Specifically, an optimum window size for image processing was determined
and a normalized likelihood function for the severe damage ratio was derived. The model was used, furthermore,
to estimate damage by integrating image data with seismic intensity data and comparing results with the actual
damage to be checked.

Journal of Disaster Research Vol.8 No.2, 2013

2. PALSAR Images and Field Survey Data
2.1. Indices Obtained from PALSAR Imagery andImage Processing
On August 15, 2007, an earthquake measuring M8.0
with an epicenter 40 km northwest of Chincha, Peru.
The city of Pisco in the Ica Region and the surrounding area were devastated by the earthquake, which left
more than 500 people dead or missing and completely destroyed more than 35,000 buildings. About 2 weeks after
the earthquake, high-resolution PALSAR imagery of thecoastal area was obtained. Figs. 1(a) and (b) shows images obtained on July 12, 2007, before the earthquake andon August 27, 2007, after the quake. The nominal ground
resolution was approximately 10 m and the image pixel
size 12.5 m.
Two indices – the difference between post- and preearthquake images and the correlation coefficients ofbackscattering coefficients – were calculated from preand post-earthquake PALSAR images. Following the accurate positioning of both pre- and post-event images, aspeckle reduction filter was applied to each image [14],then differences and correlation coefficients were calculated from Eqs. (1) and (2), below. The difference is obtained by subtracting the average value of the backscattering coefficient within an N × N pixel window of thepre-event image from that of the post-event image. The
correlation coefficient is also calculated from the same
N × N pixel window. Analysis results from the 1995
Great Hanshin-Awaji Earthquake Disaster (widely called
the Kobe earthquake) showed that differences (after – before) yielded negative values, with the spatial distribution of backscattering coefficients decreasing with building damage and as compared with that in pre-event im347

Matsuoka, M. and Estrada, M.

Fig. 2. Building damage data at Pisco based on a field survey. (a) Damage level by lot (Estrada et al., 2008). (b) Distribution of thesevere damage ratio.

agery, resulting in an overall decrease in correlation coefficient [10].
¯ i − 10 · log10 Ib¯ i
d = 10 · log10 Ia. . . . . . (1)

r=
N

N

N

N ∑ Iai Ibi − ∑ Iai ∑ Ibi

i=1
i=1
i=1




2  
2 

N
N
N
N
 N ∑ Ia2 − ∑ Iai· N ∑ Ib2i − ∑ Ibi
i
i=1

i=1

i=1

i=1

. . . . . . . . . . . . . . . . .

C1Severe Damage
Ratio D (%)
D=0C2

0 < D < 6.25

C3

6.25 ≤ D < 12.5

9.38

C4

12.5 ≤ D < 25

18.75

C5

25 ≤ D < 50

37.5

C6

50 ≤ D ≤ 100

75.0

Damage Rank

Median Value (%)
0.0
3.13

(2)

where d represents the difference in backscattering coefficients [dB], r is the correlation coefficient, and N is
the number of pixels within the window to be calculated.
Iai and Ibi represent the i th pixel values of post- and pre¯ i and Ib¯ i represent averevent images, respectively, and Ia
age values of N × N pixels surrounding the i th pixel.

2.2. Severe Damage Ratio of Buildings Based on
Field Survey Data
The target area of the damage estimation model is thecity of Pisco. Damage data used in this study were collected by members of the Japan-Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID),
National University of Engineering, Peru. CISMID personnel performed detailed on-site investigations of buildings in more than 10,000 lots just after the earthquake [15]and this was considered to be the most reliable data on
the Peru earthquake. Investigation items included building lot codes, building use, structure types, floor number,and damage level, all of which were combined with geographic information system (GIS) data. Approximately
97% of buildings in the area were masonry structures –18% adobe structures and 79% burnt brick structures –which are comparatively weak and are prone to collapse ingeneral. In the earthquake, adobe buildings were greatly
348

Table 1. Range of severe damage ratio and median values
for damage ranks.

damaged. Lots and buildings were mostly in a one-toone correspondence. For multiple buildings located onlarge lots, information on the most damaged building wasrecorded. Fig. 2(a) shows the distribution of damage levels by lot based on the field survey in Pisco.
Damage levels were classified into the following four:
Grave (Serious), Severo (Severe), Leve (Slight), and Sin
daño (No damage). Lots that could not be investigatedwere categorized as “Not available.” Grave corresponds
to G5 in the classification of the European Macroseismic
Scale (EMS-98) [16], Severo to G4 and G3, Leve to G2,and Sin daño to G1. The damage ratio was calculated
based on these GIS data. In order to take sizes of lots, including vacant lots, into consideration and to calculate reliable damage ratios, the city of Pisco was split into a gridof 3.75 × 3.75 arc-seconds (approximately 120-m grid)and estimation was performed only on grids with 10 ormore lots. The severe damage ratio of buildings in a grid
was calculated as the ratio of the number of Grave to thetotal number of buildings in the grid. The severe damage
ratio was classified into the following six damage ranks:
C1, 0% severe damage ratio in a grid; C2, more than 0%
and less than 6.25%; C3, 6.25% or more and less than
12.5%; C4, 12.5% or more and less than 25%; C5, 25%
or more and less than 50%; and C6, 50% or more. TaJournal of Disaster Research Vol.8 No.2, 2013

Development of Earthquake-Induced Building Damage Estimation Model
Based on ALOS/PALSAR Observing the 2007 Peru Earthquakeble 1 shows the correspondence of damage rank, severe
damage ratio and median values. The distribution of thesevere damage ratio is shown in Fig. 2(b).

3. Estimation of Severe Damage Ratio Basedon Regression Discriminant Function
3.1. Influence of Window Size on Accuracy of Damage Discrimination
In previous studies [10–13], a speckle reduction filter
with a 21 × 21 pixel window was applied to SAR images
and differences and correlation coefficients were calculated from a 13 × 13 pixel window. Although these window sizes suited extraction of the damaged area from 30m resolution SAR imagery of an area affected by the Kobe
earthquake and field survey data in the Hanshin area [10],it was uncertain whether these window sizes would be theoptimum for extracting damage in a city in Peru, wherebuilding types are different from those in the Hanshinarea, with approximately 10-m resolution, which is thesame as PALSAR imagery resolution. Accordingly, thechange in the accuracy of damage discrimination was examined by varying speckle reduction filter size and calculation window size for Pisco data sets.
The influence of speckle reduction filters was examined
first. A Lee filter size [14] that is variable from 3 × 3 to
51 × 51 pixels was applied to pre- and post-event images
and differences and correlation coefficients were calculated based on Eqs. (1) and (2). Images of differences
and correlation coefficients were overlaid on field survey
data and 800 pixels were randomly extracted from areas
corresponding to each of the six damage ranks shown in
Table 1 (4,800 pixels in total) to create a training sample. For quantitative evaluation of the severe damage ratio, regression discriminant analysis [17] – a method ofmultiple-group discriminant analysis that uses differences
and correlation coefficients of the six damage ranks – wasapplied. Window sizes of 7 × 7, 13 × 13, and 21 × 21 pixels were examined to calculate differences and correlation
coefficients. Fig. 3 shows the correlation ratio of regression discriminant functions representing the ability to discriminate six damage ranks against the pixel dimension
calculated from the size of the speckle reduction filter. In
this figure, a larger correlation ratio means better discriminant ability of damage ranks. The relationship between
pixel dimension and correlation ratio is slightly complicated because as the size of the filter increases, the correlation ratio decreases, but turns upward at 15 × 15 pixels.
The correlation ratio obtained when the filter size is increased to the largest one of 41 × 41 pixels is almost thesame as that obtained without any filter, however, so it
was determined that no filter would be used in this study.
The influence of window size on the accuracy of discrimination was examined next. The correlation ratioof regression discriminant functions was calculated using varying window size from 3 × 3 to 51 × 51 pixels to
calculate differences and correlation coefficients. Fig. 4
Journal of Disaster Research Vol.8 No.2, 2013

Fig. 3. Relationship between the size of speckle reduction
filters and the correlation ratio.

Fig. 4. Relationship between the calculation window size
and the correlation ratio.

shows correlation ratio against pixel dimension calculated from window size. Window size increased from3 × 3, so the correlation ratio increased, reaching a limit
at around 13 × 13 pixels. This was also found for data
sets in the Hanshin area before and after the Kobe earthquake [10]. The reasons why the correlation ratio increases includes the fact that damaged building groups
spread to some extent and, in addition, backscattering ofeach damaged building has a spatial extent. Interestingly,
although the pixel size of SAR images in the Hanshinarea and Pisco are different, the window size at which
the correlation ratio reached a limit was the same, i.e.,
at around 13 × 13 pixels. Although this is considered toarise from complex factors such as the fact that different
damage situations are involved, the detail remains a challenge to be addressed. It should be noted that although
the correlation ratio reached a maximum in Fig. 4 when349

Matsuoka, M. and Estrada, M.

Fig. 6. Normal frequency distribution model of discriminantscore ZRp .

Fig. 5. Relationship between differences in backscattering
coefficients and correlation coefficients for damage ranks.

21 × 21 pixels were used, the value is almost the same asthat for 13 × 13 pixels. A larger window size, however,does not offer the capability for detecting small spatialchanges within a window, so we decided in our study touse a 13 × 13 pixel window, which was also used in theprevious study.

3.2. Derivation of Regression Discriminant Function and Likelihood Function
To calculate difference d and correlation coefficient r,
a window size of 13 × 13 pixels was adopted as the optimum size to interpret damage in Pisco based on the analysis of data sets, and no speckle reduction filter was used.
Fig. 5 shows a scatter diagram by damage rank. The regression discriminant function calculated from the two indices is shown in Eq. (3):ZRp = −0.089 d − 2.576 r . . . . . . . . (3)where ZRp represents the discriminant score derived fromPALSAR imagery. While coefficients of d and r in discriminant score ZR j derived from JERS-1/SAR imageryof the Kobe earthquake were −1.277 and −2.729, respectively [13], coefficients derived here from PALSAR
imagery of Pisco were −0.089 and −2.576. These coefficients indicate the degree of influence of d or r on the discriminant score. Comparison between the Hanshin area
and Pisco demonstrated that the coefficient of r, the correlation coefficient, was almost the same in the two regions,but the coefficient of d, the difference in backscattering
coefficients, of Pisco was small, or approximately zero,compared with that of the Hanshin area. The influence ofd on discrimination of the damage rank in Pisco is therefore negligible.
Next, a likelihood function for estimating the severe
damage ratio from discriminant score ZRp is formed
following procedures similar to those in the previous
study [11, 13]. The likelihood function in this study
350

Table 2. Average values and standard deviations in the likelihood function of data in PALSAR intensity imagery.
Damage Rank

Average of ZRp

Standard Deviation

C1-1.4700.323

C2-1.3550.291

C3-1.3320.281

C4-1.2000.331

C5-1.0520.415

C6-0.8870.484

means the probability of being in each damage rank whenZRp is given. Specifically, the frequency distribution of
ZRp of 800 randomly extracted pixels from each damage
rank is modeled as a normal distribution. Fig. 6 shows
normal distribution models (likelihood function). Table 2
shows average values and standard deviations of ZRp for
individual damage ranks. The higher the damage rank,
the larger the discriminant score ZRp . Because distribution curves of some damage ranks cross in regions withlow discriminant scores, however, discrimination in areas
with low damage ranks is not possible. Fig. 7 shows normalized likelihood functions in which the sum of the likelihood of all damage ranks in Fig. 6 becomes 1.0. For regions where ZRp is −2.2 or less, a constant value obtainedby extrapolating the value at ZRp = −2.2 is used in order
to avoid reversing the sequence of the severe damage ratio caused by distribution curves crossing. Average values
and standard deviation of the estimated severe damage ratio against discriminant score ZRp are thus obtained frommedian values of the damage rank in Table 1 and the distribution shown in Table 2 and Fig. 7. Fig. 8 shows curvesof average values and average values ± standard deviationof the severe damage ratio estimated from ZRp . The severe
damage ratio increases with increasing ZRp . Because thediscriminant score was adjusted to make the constant term
zero, relative positions on the horizontal axis in Fig. 8 arearbitrary. Taking this into account, the normalized likelihood function derived from field survey data and PALSAR imagery of Pisco gives a high severe damage ratio
Journal of Disaster Research Vol.8 No.2, 2013

Development of Earthquake-Induced Building Damage Estimation Model
Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

Fig. 7. Normalized likelihood function of discriminantscore ZRp .

Fig. 9. Discriminant score ZRp estimated from PALSAR
imagery.

Fig. 8. Relationship between the severe damage ratio (average values and standard deviations) and the discriminantscores of Pisco ZRp and Hanshin ZR j .

(average value) in regions with low discriminant scorescompared with that derived from field survey data andJERS-1/SAR imagery of the Hanshin area [13] (shown
together in Fig. 8). It also indicates that small changes
in backscattering characteristics have a large influence on
the estimation of the severe damage ratio. This could be
influenced by differences in building damage situations
between Pisco and the Hanshin area.
Figure 9 shows the ZRp distribution obtained from preand post-earthquake PALSAR images, and Fig. 10 shows
the severe damage ratio (average values) estimated from
ZRp . ZRp values and the severe damage ratio are slightly
larger at the center of Pisco. It should be noted that thetarget area is restricted to urban areas where the cardinal
effect can be expected, therefore, areas whose backscattering coefficients are small (−5 dB or under) in pre-event
images are masked. The distribution of the severe damage
ratio of Pisco city estimated based on PALSAR imagery
agrees well with field survey data. Areas with high severe
damage ratios are found in sections of farmland, however,
because they are not adequately masked owing to variations in backscattering characteristics caused by vegetation.

Journal of Disaster Research Vol.8 No.2, 2013

Fig. 10. Severe damage ratio of buildings estimated fromthe normalized likelihood function (average values).

Fig. 11. Relationship between seismic intensity and averagevalues and standard deviations in the fragility function.

351

Matsuoka, M. and Estrada, M.

Fig. 12. Estimated severe damage ratio obtained by data integration of PALSAR imagery and seismic intensity
data: (a) average values, (b) standard deviations.

4. Severe Damage Ratio Estimation by Integration with Seismic Intensity Data
In a case where only PALSAR imagery is used and
ZRp < −2.2, distributions overlap as shown in Fig. 6
and contain only slightly more information than complete
noninformation. In other words, the average severe damage ratio was 13.0% with a standard deviation of 21.0,
compared to an average severe damage ratio of 35.4% anda standard deviation of 31.2% when an equal probabilityof 1/6 is given for each damage rank. As is shown in
Fig. 10, a severe damage ratio of approximately 20% isestimated even in broadly undamaged areas. Seismic intensity data is therefore used as supplementary data forhighly accurate estimation in all regions, including regions of low severe damage ratio. A fragility function interms of seismic intensity data has been established basedon data obtained from the Kobe earthquake [11]. An improved fragility function that considers the fragility ofmasonry buildings in Peru has also been developed [13].
Fig. 11 shows the fragility function for Peru used in thisstudy and that for the Hanshin area for comparison. It
corresponds roughly with previous fragility functions [18,19]; the severe damage ratio of buildings in Peru becomes
higher than that in the Hanshin area if buildings are affected by earthquakes of the same seismic intensity.
In line with the work of Nojima et al. [11], Bayesian
updating theory has been used to improve the accuracyof estimating the severe damage ratio. It does so by integrating PALSAR imagery ZRp with seismic intensity data.
Specifically, the probability of being in each damage rank
is updated by multiplying the probability of being in eachdamage rank when seismic intensity data is given with the352normalized likelihood of ZRp of each damage rank (equivalent to the probability of being in each damage rank whenZRp is given), and then normalizing (that is, constraining
the sum of probabilities to be one) [11]. Figs. 12(a) and(b) shows average values and standard deviations in thesevere damage ratio based on the probability updated forthe 7 × 7 = 49 combinations of the two indices. Compared with Kobe earthquake results [13], seismic intensity data contribute to estimating the severe damage ratio
where ZRp of SAR imagery is low, so the severe damage
ratio increases steeply and the seismic intensity data contribution is nearly zero where SAR imagery ZRp is high.
U.S. Geological Survey (USGS) ShakeMap [20] wasused for earthquake seismic intensity data. Peak Ground
Velocity distribution data from ShakeMap [21] was converted by Fujimoto and Midorikawa [22] into measured
seismic intensity, and is shown superimposed on PALSAR imagery in Fig. 13. Large shaking of 6− to 6+
on the Japan Meteorological Agency (JMA) seismic intensity scale is found all over the image. The result of
estimating the severe damage ratio by data integrating
discriminant score ZRp data obtained from PALSAR imagery (Fig. 9) and measured seismic intensities (Fig. 13)
is shown in Fig. 14(a). An enlarged view of the Pisco area
is shown in Fig. 14(b). Because built-up area is the target,
areas with backscattering coefficients of −5 dB or lessare masked. Fig. 15 shows enlarged views in the Pisco
area of (a) distribution of the severe damage ratio investigated by the field survey, (b) the same distribution estimated from PALSAR imagery alone, and (c) the same
distribution based on the data integration of PALSAR imagery and seismic intensity data. Distribution of the severe damage ratio estimated based on the data integration

Journal of Disaster Research Vol.8 No.2, 2013

Development of Earthquake-Induced Building Damage Estimation Model
Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

Fig. 13. Distribution of measured seismic intensity estimated from ShakeMap.

Fig. 14. Estimated severe damage ratio obtained by data
integration of PALSAR imagery and seismic intensity data
(average values).

of PALSAR imagery and the seismic intensity data is ingood agreement with the distribution based on the fieldsurvey, especially in areas with a high severe damage ratio in the central part of Pisco. looking at a broad area
all over Pisco, the area overestimated by estimation basedon PALSAR imagery alone could be reestimated appropriately. That is, it would be estimated at the level ofthe actual severe damage ratio by using estimation basedon data integration with seismic intensity data. When themodel based on the Kobe earthquake was applied to Pisco
without improvement, damage in the western coastal areaof Pisco could not be detected [13]. The model estabJournal of Disaster Research Vol.8 No.2, 2013

Fig. 15. Comparison of distribution maps of the severe damage ratio in Pisco (enlarged views): (a) field survey, (b) estimation based on PALSAR imagery alone, and (c) estimation
based on the data integration of PALSAR imagery and seismic intensity.

lished in this study, however, could extract damage in thisarea. This fact suggests that, in order to accurately estimate damage areas, it is important to use a damage extraction model suited to the target area because there aredifference in urban structures, building types, and damage
situations between Japan and Peru.

5. Conclusions
To develop a technology to quickly assess areas affected by earthquakes using imagery of L-band synthetic
aperture radar (SAR) mounted on satellites, an estimation model of the severe damage ratio of buildings, op353

Matsuoka, M. and Estrada, M.

timized for Peru, has been created based on field survey
data and PALSAR imagery of the city of Pisco struck by
the 2007 Peru earthquake. Regression discriminant analysis has been performed in which explanatory variables,
namely differences and correlation coefficients, were calculated from pre- and postearthquake PALSAR images.
Target groups were six damage ranks classified based on
the severe damage ratio of buildings. Examination of an
optimum window size for calculation of the differences
and correlation coefficients have resulted in the same parameter as that obtained from data from the 1995 Kobe
earthquake. A regression discriminant function has been
derived that considers urban structure, building type, thedamage situation in Peru, and the pixel resolution of PALSAR imagery. Furthermore, normalized likelihood functions for the severe damage ratio have been obtained fromthe discriminant score of the regression discriminant function. It has also been demonstrated that the distribution of
the severe damage ratio could be estimated at high accuracy based on data integration of PALSAR imagery with afragility function based on the seismic intensity data of thePeru earthquake. Similar to PALSAR, an L-band SAR,
called PALSAR-2 will be mounted on a satellite, ALOS2, to be launched in 2013. If an earthquake occurs in Peru
in the future, the model proposed in this study could be
used for damage assessment. By preparing a highly reliable fragility function that fits the actual situation of buildings in Peru and integrating this with seismic intensity
data based on strong-motion observation and simulation,
we will be able to assess damage more accurately.
Acknowledgements
PALSAR data are the property of Japan’s Ministry of Economy,
Trade and Industry (METI) and the Japan Aerospace Exploration
Agency (JAXA) and have been processed using the GEO Grid of
the National Institute of Advanced Industrial Science and Technology (AIST). This study was supported in part by a Scienceand Technology Research Partnership for Sustainable Development (SATREPS) project titled Enhancement of Earthquake andTsunami Disaster Mitigation Technology in Peru (Principal Investigator: Prof. Fumio Yamazaki); a New Energy and Industrial
Technology Development Organization (NEDO) Industrial Technology Research Grant Program, titled Development of realtime
tsunami damage detection technology for expeditious disaster response of Japan and ASEAN countries (Leader: Prof. Shunichi
Koshimura); and a grant in aid for scientific research (Research
No. 21310119; Principal Investigator: Prof. Fumio Yamazaki).
We would like to express our gratitude for these contributions.

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354

Journal of Disaster Research Vol.8 No.2, 2013

Development of Earthquake-Induced Building Damage Estimation Model
Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

Name:

Name:

Masashi Matsuoka

Miguel Estrada

Affiliation:

Affiliation:

Associate Professor, Department of Built Environment, Tokyo Institute of Technology

General Director, CISMID
Associate Professor, Faculty of Civil Engineering, National University of Engineering

Address:

Address:

Nagatsuta 4259-G3-2, Midori-ku, Yokohama 226-8502, Japan

Av. Tupac Amaru 1150, Rimac, Lima, Peru

Brief Career:

Brief Career:

1992 Research Associate, Tokyo Institute of Technology
1996 Engineer, Remote Sensing Technology Center of Japan
1998 Deputy Team Leader, RIKEN
2004 Team Leader, National Research Institute for Earth Science andDisaster Prevention
2007 Senior Research Scientist, National Institute of Advanced Industrial
Science Technology
2010 Division Chief, National Institute of Advanced Industrial Science
Technology
2012- Associate Professor, Tokyo Institute of Technology

1998-2000 Master of Engineering in the field of Civil Engineering, The
University of Tokyo
2000-2004 Ph.D. of Civil Engineering, The University of Tokyo
2004-present Associate Professor, Faculty of Civil Engineering, National
University of Engineering
2013-present General Director, Japan-Peru Center for Earthquake
Engineering Research and Disaster Mitigation (CISMID), Faculty of Civil
Engineering, National University of Engineering

Selected Publications:• Matsuoka and Yamazaki, “Use of Satellite SAR Intensity Imagery for
Detecting Building Areas Damaged due to Earthquakes,” Earthquake
Spectra, EERI, Vol.20, No.3, pp. 975-994, 2004.
• Matsuoka and Nojima, “Building Damage Estimation by Integration ofSeismic Intensity Information and Satellite L-band SAR Imagery,” Remote
Sensing, MDPI, Vol.2, No.9, pp. 2111-2126, 2010.
• Matsuoka and Yamazaki, “Comparative Analysis for Detecting Areas
with Building Damage from Several Destructive Earthquakes Using
Satellite Synthetic Aperture Radar Images,” Journal of Applied Remote
Sensing, SPIE, Vol.4, 041867, 2010.

Selected Publications:• M. Estrada, H. Miura, F. Yamazaki, and S. Midorikawa, “Evaluation ofSocial Seismic Vulnerability through High Resolution Satellite Imagery,”
15th World Conference on Earthquake Engineering, Portugal, 2012.
• M. Estrada, C. Zavala, and Z. Aguilar, “Use of Geomatics for Disaster
Management – Case Study 2007 Peru, Pisco Earthquake,” 7th International
Workshop on Remote Sensing and Disaster Response, USA, 2009.
• M. Estrada, M. Matsuoka, and F. Yamazaki, “Use of Optical Satellite
Images for the Recognition of Areas Damaged by Earthquakes” 6thInternational Conference on Seismic Zonation, USA, 2000.

Academic Societies & Scientific Organizations:• Peruvian Board of Engineers• Earthquake Engineering Research InstituteAcademic Societies & Scientific Organizations:• Earthquake Engineering Research Institute (EERI)• Architectural Institute of Japan (AIJ)• Remote Sensing Society of Japan (RSSJ)Journal of Disaster Research Vol.8 No.2, 2013355