Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image

Paper:

Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image
Tomoyo Hoshi∗1 , Osamu Murao∗2 , Kunihiko Yoshino∗3 ,Fumio Yamazaki∗4 , and Miguel Estrada∗5∗1 Shimizu Corporation, Japan∗2 International Research Institute of Disaster Science, Tohoku University468-1 Aramaki-aza-Aoba, Aoba-ku, Sendai, Miyagi 980-0845, Japan
E-mail: murao@irides.tohoku.ac.jp∗3 Faculty of Engineering, Information and Systems, University of Tsukuba, Japan∗4 Department of Urban Environment Systems, Chiba University, Japan∗5 Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation (CISMID),
National University of Engineering, Peru
[Received July 16, 2014; accepted September 9, 2014]

Pisco was the area most damaged by the 2007 Peru
earthquake. The purpose of this research is to develop
possibilities of using satellite imagery to monitor postdisaster urban recovery processes, focusing on the urban change in Pisco between 2007 and 2011. To thisend, the authors carried out field surveys in the city
in 2012 and 2013 and also examined previous surveys
to determine that building reconstruction peaked between 2008 and 2009. After analyzing the five-year recovery process, the authors compared its reconstruction conditions by visual interpretation with those byimage analysis using satellite image. An accuracy of
71.2% was achieved for the visual interpretation results in congested urban areas, and that for developed
districts was about 60%. The result shows that satellite imagery can be a useful tool for monitoring andunderstanding post-disaster urban recovery processes
in the areas in which conducting long-term field survey is difficult.
Keywords: post-disaster recovery process, building damage examination, image analysis, urban development, error matrix analysis

1. Introduction
1.1. BackgroundIn the research field of urban recovery, it is important to monitor urban recovery processes after disasters.
Through recovery monitoring, it is possible to understandthe recovery process of affected area and to evaluate theurban recovery strategies implemented by governments
after disasters. Moreover, quantitative evaluation of therecovery process can lead to suggestions of better recovery strategies for other cities in the future by considering
the relationship between recovery process and strategies.
Indices based on the economic recovery level such as
gross domestic product or electric power consumption
Journal of Disaster Research Vol.9 No.6, 2014

can be used to quantitatively evaluate urban recovery processes. Another type of index is from the perspective ofrecovery of the physical environment, which is related toinfrastructure, building construction, and urban development.
Research groups led by Murao proposed a method fordrawing a building recovery curve to quantitatively evaluate urban recovery processes. For example Murao andNakazato [1] compared regional differences based on permanent housing construction data in Sri Lanka, and Murao et. al [2] clarified national differences in recovery processes among Sri Lanka, Thailand, and Indonesia after the2004 Indian Ocean Tsunami.
However, such curves were constructed on the basis ofstatistical data provided by governments or data obtainedby the researchers by elaborate field surveys. Thus, collection of proper datasets for construction of these recovery curves is very time-consuming and costly.
As an alternative, remote sensing technology may provide a reasonable method for data acquisition.
This research considers the possibilities of satellite image usage to monitor post-disaster urban recovery processes focusing on urban change in Pisco, which is thearea most devastated by the 2007 Peru earthquake.

1.2. Existing Related Research
After an earthquake, building damage estimation would
be helpful for emergency response and the subsequent recovery stage. Some research groups have considered usage of satellite images for this purpose.
For example, Koarai et.al [3] analyzed large-scale geological hazards in the case of the Northern Pakistan earthquake, the Middle Java earthquake and the Leyte landslideby using optical high-resolution satellite imagery such as
QuickBird, IKONOS, ALOS and SPOT5. According to
the research, 1 m-resolution stereo images are necessary
to recognize building damages in urban areas.
Matsuzaki et.al [4] performed visual damage interpre1059

Hoshi, T. et al.

Fig. 1. Building damage conditions owing to the 2007 Peru earthquake, as reported by the Japan–Peru Center for Earthquake
Engineering Research and Disaster Mitigation.

tation of buildings in Pisco by using QuickBird satellite
Images following the 2007 Peru earthquake and compared
the results with that of field survey data. Then, the accuracy of damage inspection was investigated. Finally theyreported that the accuracy of the damage grade depends
on factors such as the influence of building shadows, differences in the time of data acquisitions, and local building/environmental conditions.
However, few post-disaster recovery processes havebeen tracked by using image analysis. Nakaoka andNakao [5] attempted to understand recovery conditions
in Kobe after the 1995 Great Kobe earthquake using hue
information of satellite images. Higashida et.al used acharge coupled device (CCD) camera to monitor the recovery conditions of some of the areas affected by the
1995 Great Kobe earthquake and the 1999 Chi-Chi earthquake, and they proposed the use of a Recovery Index [6].
However, the areas covered by the method were limited.

1.3. Purpose and Research Method
With a focus on Pisco, this research aims to understandthe chronological changes of post-disaster urban conditions and to develop the possibilities of satellite imagery
as a useful tool for long-term post-disaster recovery monitoring.
To this end, the following procedure was conducted.
First, an outline of building damage and reconstruction
conditions in Pisco as of 2012 are reported based on ourfield surveys and literature surveys.
1060

Second, the image analysis method is considered by acomparative study of field survey data and satellite images of chronological changes of town blocks from 2007
to 2011. By selecting three characteristic districts, texture
analysis is attempted.
Finally, we examine the results of the above methodby using accuracy analysis. Then the possibilities of using satellite imagery for monitoring post-disaster urban
recovery process are considered.

2. Outline of Building Damage and Reconstruction Conditions in Pisco as of 2012
2.1. Outline of Building Damage in Pisco Owing to
the 2007 Peru Earthquake
The Pisco earthquake occurred on August 15, 2007. In
the Ica region, which was the area most severely damagedby the earthquake, 134,312 houses were affected. Pisco
district is located in the middle of Pisco province in thisregion. The number of affected houses in the district was
14,008, including 4,106 collapsed buildings [7].
Just after the earthquake, numerous damage surveys
were conducted, and damage conditions were reported
by some institutes such as the Earthquake Engineering Research Institute (EERI) [8], and the joint investigation group by the Japan Society of Civil Engineers(JSCE) and Japan Association for Earthquake Engineering (JAEE) [9]. Fig. 1 shows the building damage
Journal of Disaster Research Vol.9 No.6, 2014

Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image

A
A

B

B

©GoogleMap

Fig. 3. Residential areas selected for hearing survey corresponding to that shown in Fig. 2.

Fig. 2. Blocks included in our field survey. Location of
Picture A and Picture B are shown in Fig. 3.

conditions investigated by the Japan–Peru Center for
Earthquake Engineering Research and Disaster Mitigation (CISMID).

2.2. Recovery Condition as of 2012
The authors conducted a field survey in July 2012 tounderstand the post-earthquake urban conditions of Pisco.
They compared building recovery conditions based on visual interpretation by survey and satellite images from
GeoEye captured in 2010 with the damage conditions reported by CISMID [10]. The results indicated that 93.2%of the seriously or severely damaged buildings had beenreoccupied and that number of new houses had increased
in newly developed areas planned by the government [11].

lots had been reconstructed, which means that reconstruction ratio in the districts was 99.3%. The structure of
77.8% of the reconstructed buildings was stronger than
adobe.

3.2. Hearing Surveys in Residential Areas
City planning has resulted in expanded residential areas [11]. In order to understand the individual recovery
processes of different types of residences, we conductedhearing surveys in five residential areas including Leticia,
Alto el Morino, Dios Te Ama, Renacer, and La Arborada.
Fig. 3 shows these areas, which correspond to the covered
areas in Fig. 1. The number of interviewees was three ineach area, for a total of fifteen. The Questionnaire contents were about structural type of their houses before andafter the disaster, reasons for moving, previous house condition, monetary support by the government and its contents, necessary cost and time for resettlement, and timeof moving to new buildings.
Based on the hearing surveys, the following results
were revealed:

3. 2013 Field Survey of Recovery Conditions(1) Three types of housing loan were offered.

3.1. Recovery Conditions in Central Districts
The authors carried out another field survey from on
July 11 to 15, 2013, to assemble information with moredetail.
According to a survey conducted by the Pisco city government, eleven blocks were occupied by mostly adobestructure buildings [12]. These blocks were characteristicof the central districts of Pisco and were devastated by
the earthquake and characteristic of the central districtsof Pisco. We assumed that these destroyed buildings had
been replaced with new solid-structure buildings such as
rain-forced concrete buildings or brick buildings. We thenconducted a field survey by visual interpretation to examine the recovery conditions of the town blocks. The elevenblocks that were congested by adobe structures are shownin Fig. 2.
According to the CISMID investigation in 2007,
474 land lots contained destroyed buildings in the blocks.
Our field survey indicated that the buildings in 471 land

(2) Financing and the interest rate were determined by
the level of income of creditor.

Journal of Disaster Research Vol.9 No.6, 2014

(3) A residence certificate was necessary for accepting
monetary support from the government.
The residents of Pisco included many squatters wholacked residence certificates. Therefore, it was difficult
for these people to rebuild even thought the earthquake
severely damaged their houses. However, the government
granted legal residence after the earthquake to those who
had been living illegally in the city for certain period oftime. As a result of this policy, many squatters begin tolive in the newly developed areas legally.
Residence certificates were examined by Organismo
de Formalización de la Propiedad Informal (COFOPRI).
Fig. 4 shows changes in the number of certificates granted
from 2007 to 2013 in the Ica region, which includes Pisco
province [13]. This chart indicates that illegal residents
had gained residence certificates and had been living in
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Hoshi, T. et al.

Table 1. Satellite image specifications.

Number of
Cer ficate Grant

Fig. 4.
Changes in numbers of residence certificates
granted [13].

Satellite
SensorDateobtained
Coordinate
SystemProjection
Coordinate
Image size
Resolution
Off-nadirangle2

QuickBird
BGIS 2000
Dec. 13, Nov. 9, July 1,
2007
2008
2009

World View 2WV110
Aug. 28, July 15,
2010
2011

Geographic Lat/Lon
WGS-8476 − 13 18.95 W, 13 − 41 54.74 S
7,344 by 4,815
0.6 m
0.5 m
1.28

11.56

19.99

7.78

17.74

the newly developed areas for two years following the2007 Peru earthquake. Thus, these statistical data obtained by using satellite images can be objective criteria
for evaluating recovery processes.

4. Consideration of Image Analysis Method for
Examining the Recovery Process
The aforementioned field survey is a method used forunderstanding post-disaster recovery processes. However,
the economic and temporal expenses are considerable.
Therefore, we consider a possibility of using satellite image to understand recovery processes as an alternative
measure in this section.

4.1. Satellite Image Used
We obtained panchromatic images with a resolution of
about 0.6 m, which are known as high-resolution satellite images, and multi-spectral images with resolutions of
1.84 m–2.62 m captured between 2007 and 2012. The
specifications of these images are shown in Table 1.
In performing this analysis, pan-sharpened images1
were produced by superimposing high-resolution images
on low-resolution color images and by subdividing pixels. Murakami and Yamashita [14] indicate that people
can accurately recognize an object of about 10 times forthe resolution in visual observation [14]. Using the pansharpened images with a resolution of 0.6 m, visual interpretation for the analysis is possible.
ENVI version 5.0 was used as the application software
for the image analysis.
4.2. Examined Districts
Prior to performing the analysis, it was necessary tounderstand characteristics of the examined area. The following factors were considered:(1) Characteristics recognized on satellite images
1. Pansharpening is a process to create a high-resolution RGB color image based on a low-resolution RGB color image and a high-resolution
monochromic image.

1062

Fig. 5. View of Pisco central district.

• Generally, building roofs appeared as white, and land

surfaces including open spaces inside private landlots appeared as black.
• Many buildings had flat roofs.
• Because the observation angle was low, shade wasreflected greater in many places.
(2) Characteristics recognized by the survey (Fig. 5)• The target area is basically earthy.
• Most buildings are one- or two-story residential.
• Most streets are unpaved except for main roads.

(3) Social context recognized by interviews• The percentage of squatters is very high.
• Even in normal times, individual residents often ren-ovate their own houses or build extensions; thus, thebuilding shape is often changed.
The research approach in subsequent sections is based on
the above findings.

4.3. Scope of Analysis and Selection of Training
Districts
Because it is easier to use simple images for analysis, we used minimum range satellite images covering the
2. Any point not directly beneath a scanner’s detectors, but rather off at anangle (ESRI GIS-Dictionary).

Journal of Disaster Research Vol.9 No.6, 2014

Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image

Table 2. Basic information of the study area image.

2007

2011

2007

2011

Number of town blocks Number of pixels Areas (m2 )629
9,897,371
3,563,053(a) vacant land to building area(c) building area to vacant land

2007

2007

2011

2011

(3) Developed Districts(2) Coastal Districts(b) building area to building area(d) vacant land to vacant land

I. Buildings as of 2011

II. Empty as of 2011

㻟Fig. 7. Four types of changing land lot usage patterns.

(1) Central Districts

Fig. 6. Scope of analysis and selected training districts.

study area indicated in Table 1. Table 2 shows basic information of the area including the number of blocks, thenumber of pixels, and the total area.
Prior to image analysis, it was necessary to examine theaccuracy of using the images. We selected the following
three areas according to damage conditions and locations(Fig. 6):

Table 3. Characteristics of image analysis methods [15].
TypeStrength
Object-based auto- Obtained detailed
matic classification classification maps,
less noise
Pixel-based auto- Relatively obtained
matic classification homogeneous
accuracy without
skillful technique
Visual interpreta- High classification
tion
accuracy

Weakness
Complex parameters setting for classification
A lot of noise inhigh-resolution
satellite images
Required skillful
technical capabilities, high temporal
cost and workload(1) Central districts, commercial with public facilities.
(2) Coastal districts, residential illegally occupied.
(3) Developed districts, residential developed after the2007 earthquake.
The comparison for understanding the recovery conditions in Section 2 is based on the damage survey conducted by CISMID in 2007, the satellite image from GeoEye captured in 2010, and the field survey conducted in2012. Then, the satellite images captured in 2007 and in2011 were used as a training data for the accuracy assessment.

4.4. Types of Change in Land Lots
In order to quantitatively evaluate the recovery processes in the studied districts, the presence of buildings
is a significant parameter. The ratio of the buildings tovacant land in each land lot was used for our evaluation.
Regarding changes in land lot usage from 2007 to 2011,
four types of changing patterns were identified: (a) vacantland to building area, (b) building area to building area,(c) building area to vacant land, and (d) vacant land tovacant land (Fig. 7). An understanding of these four typesof building–vacancy patterns is essential for analyzing thechronological recovery transition of individual land lots.

Journal of Disaster Research Vol.9 No.6, 2014

4.5. Image Analysis Method
4.5.1. Types of Image Analysis Methods
Two types of satellite image analysis methods are in
general usage. The first is object-based analysis dealing with vector image data, and the second is pixel-based
analysis dealing with raster image data. Table 3 comparesthe differences of the methods [15].
In order to decipher building damage or to extract
building outlines in high-resolution satellite image analysis, object-based analysis is an effective method, thoughproper parameters need to be set for data conversion withskillful techniques [14, 15]. We considered the possibility of using pixel-based analysis in our research because
we deal with long-term recovery processes that requirecontinuous chronological data, and we aim to establish alaborsaving methodology for urban recovery monitoringin the future.
4.5.2. Analysis Procedure
The analysis procedure we applied in this study is illustrated in Fig. 8.
As a common treatment, we first superimposed eachband of individual images on one layer after geometriccorrection, indicated as “image superposition” in the figure. By comparing the images, we recognized no difference in the urban fabric or shapes of town blocks. Then1063

Hoshi, T. et al.

Table 4. Numbers of pixels regarding changing patterns.
abcVacant land Building area Building
to building
to building
area toarea
area
vacant land
13,303
10,359
3,036

d
Total
Vacant land
to vacantland
11,74538,443

Buildings
Vacant landStreetFig. 8. Analysis flowchart used in this study.

Fig. 9. Spectrum separation conditions.

we eliminated most areas except the focus districts bymask processing.
Second, we selected remarkable changing patterns as
training data and calculated fundamental statistics to evaluate statistical significance using pixel-based analysis andtexture analysis.
Finally, as evaluated by the t-test, we conducted maximum likelihood classification based on the training dataand classified changing pattern. Unclassifiable areas withshadows were eliminated by Iterative Self-organizing
Data Analysis Technique (ISODATA) clustering.

4.6. Extraction of Pixels Regarding Changing Patterns as Training Data
No ground truth data for the objective areas existed.
Then, we identified changing areas by visual observationon the superimposed images as of 2007 and 2011. Table 4shows the numbers of pixels regarding changing patterns.
Checking spectrum separation by calculating fundamental statistics reveals that the scatter plot area for buildings and that for vacant land overlapped (Fig. 9), whichindicates that the spectrum separation performance was
not very high for this analysis.
Next, we used fundamental statistics based on entropy,
or the texture information of the images, to reduce misclassification caused by using only pixel-based classification. The higher the level of tint, the more entropy
increases. Entropy decreased in the area surrounded by
the red circle in Fig. 10. As shown in Fig. 11, a building existing in 2007 was removed in 2011. On the contrary, entropy increased in that surrounded by the blue circle in Fig. 10, which indicates new building construction.
Therefore, we can classify the patterns in listed in Table 4
by using this method.
4.7. Setting Window Size
By setting the window size at more than one pixel, we
can obtain texture information. One pixel on a 0.6 m resolution image covers about 60 cm2 in the real world. The
proper window size for this analysis was that slightly covering the area of a building. Because of a lack of building
1064

2007

2011

Fig. 10. Comparison on image represented by entropy.

2007

2011

Fig. 11. Comparison of satellite images.

outline polygon data, we extracted 100 buildings at random on the image and calculated their average length andwidth. Although the lengths of the building varied, theirwidth was almost 7 m. Therefore we set the window size
at 13 pixels by 13 pixels for texture analysis, which equals
7.8 m2 .

5. Examination of Recovery Process Analyzed
by the Method
5.1. Combination of Target TimeThe target times for examining the changing conditions
include the following combinations: (1) 2007 and 2011,(2) 2007 and 2008, (3) 2008 and 2009, (4) 2009 and 2010,
and (5) 2010 and 2011.
Journal of Disaster Research Vol.9 No.6, 2014

Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image

5.3. Examination of Proposed Analysis Method
Three training districts were previously selected, as
shown in Fig. 7. We used the results of the analysis forthese districts to examine the accuracy of the method proposed in the present study.
5.3.1. Changes in Central Districts
Changes in the central districts as determined by satellite images captured in 2007 and 2011 in addition to theresults of the analysis are shown in Fig. 13. It appears thatsome empty lots remained in the image of 2007 where collapsed buildings had been removed. The analysis results
verified relatively large sites with some buildings became
vacant lots as of 2011. Large buildings were effectively
extracted from the images, though some boundaries between buildings and open space may have been misclassified.
5.3.2. Changes in Coastal Districts
Changes in coastal districts are shown in Fig. 14. These
districts were severely damaged by the 2007 Peru earthquake. The government provided temporary housing to
the victims in the districts. However, many squatters reconstructed their houses without government aid. As aresult of the analysis, the remaining foundations of suchdamaged buildings were misclassified as sound buildings.

Analysis Result
2007/2008 Buildings
2007/2008 Vacant㻝㻟

5.2. Recovery Process Analyzed by Our Method
The method described in Section 4 was applied to theobjective area. The images in Fig. 12 show the chronological changes in Pisco from 2007 to 2011 as determinedby this analysis.
Each image presents differences detected between
spans of one year. Buildings are presented in black, andvacant land is in gray. It is possible to recognize building
density conditions and transitions of housing constructionin these images. For example, the area surrounded by the
red square in each image shows obvious changes in the
districts since 2007.

Analysis Result
2008/2009 Buildings
2008/2009 Vacant

Analysis Result
2009/2010 Buildings
2009/2010 Vacant

Analysis Result
2010/2011 Buildings
2010/2011 Vacant

5.3.3. Changes in Developed Districts
Changes in the developed districts are shown in Fig. 15.
This area, which was a wasteland in 2007, was developedby the government as a permanent housing area following
the earthquake. Although this change is obvious throughvisual observation, misclassification often occurred in theanalysis. Such results may be attributed to changes in thecomponents on the ground owing to the development. In
particular, concrete that replaced the soil in the new residential area was recognized in the analysis as the samematerial used for building roofs.

Fig. 12. Changes in the entire area of Pisco.

5.4. Accuracy Assessment of Analysis Results
Because no building polygon data was available for thefocus area, we used the training data for accuracy assessment. By overviewing the images for 2007 and 2011, we

deciphered changes in individual land lots by visual interpretation based on the four types of classification listed inTable 4, and we calculated the number of changed pixels.
Next, error matrix analysis was used for accuracy as-

Journal of Disaster Research Vol.9 No.6, 20141065

Hoshi, T. et al.

2007

2011

Analysis Result

2007

Fig. 15. Changes in developed districts in 2007 and 2011.

used for producer’s accuracy to indicate the accuracy rateof visual interpretation. Conversely, C12 /C13 and C22 /C23
are used for user’s accuracy to indicate the accuracy rateof image analysis results. (C11 + C22 )/C33 is used as indicate overall accuracy for a comprehensive evaluation.
The producer’s low accuracy presents extraction failure
by misclassifications. On the contrary, the user’s low accuracy indicates low accuracy of the results obtained byimage analysis.
As shown in Tables 6–8, overall accuracy for the central districts was more than 70%; that for coastal and developed districts was about 65% and 60%, respectively.
Koshimura et al. conducted research on post-tsunami
image analysis in Banda Aceh. They reported that accuracy by pixel-based analysis was about 67%, and that by
object-based analysis was about 71% [16]. Matsuzaki etal. reported analysis results in Pisco at about 70% [4].
Our research obtained comparable results in the central
districts.
The user’s accuracy for vacant land was relatively
higher than that for buildings in all districts. It is considered that the spectral information of a vacant lot is morestable than that of buildings.

2011

Analysis Result
2007/2011 Buildings
2007/2011 VacantFig. 13. Changes in central districts in 2007 and 2011.

Analysis Result
2007

2011

2007/2011
Buildings
2007/2011
VacantFig. 14. Change of coastal districts (2007 and 2011).

sessment of the image analysis, as show in Table 5. Eachcolumn in the table indicates the number of pixels basedon visual interpretation, and each row indicates that determined by the image analysis. C11 /C31 and C22 /C32 are
1066

5.5. Quantification of Recovery Processes
Quantitative evaluation of the recovery processes in
Pisco requires calculation of buildings. However, the individual building outlines in the images and maps used inthis research were not clear. Therefore, the building area
in each land lot was estimated using the map developedby CISMID, as shown in Fig. 1. By using 1% land lot
sampling of the entire area, the average building coverage
ratio per lot was calculated. Then, the total building areaof the focus area of Pisco was defined as the initial value
for evaluating the recovery processes.
Figure 16 shows a quantitative recovery process of
Pisco as a result of this effort. Compared with the conditions present four months after the earthquake in 2007, thebuilding ratio increased slightly in 2008. The ratio sharply
increased between 2008 and 2009 and rose slightly in
2011. The building ratio in 2010, however, was lower
than that in 2009 and in 2011. It is inferred that the highobservation angle for the satellite image of 2009 affectedto the analysis by increasing the ratio of building shadows.
A comparison of the number of certificates granted(Fig. 4) with the recovery process tendency (Fig. 16) revealed the actual post-earthquake recovery situation in
Journal of Disaster Research Vol.9 No.6, 2014

Post-Disaster Urban Recovery Monitoring in Pisco
After the 2007 Peru Earthquake Using Satellite Image

Table 5. Table of error matrix analysis.

Image
Analysis

Building
Vacant
Total

Building
C11
C21
C31

Visual interpretation
Vacant
Total
C12
C13
C22
C23
C32
C33

PA

C11 /C31

C22 /C32-UA
C12 /C13
C22 /C23
SA:(C11 +C22 )/C33

UA: user’s accuracy, PA: producer’s accuracy, SA: system accuracy

Table 6. Analysis results for central districts.

Image
Analysis(Pixels)

Building
VacantTotal
PA

Building
66,701
10,27776,978
86.6%

Visual interpretation (Pixels)
VacantTotal
38,454105,155
53,85064,12792,304
169,282
58.3%
-

UA
63.4%
84.0%
71.2%

UA: user’s accuracy, PA: producer’s accuracy, SA: system accuracy

Table 7. Analysis results for coastal districts.

Image
Analysis(Pixels)

Building
VacantTotal
PA

Building
21,7202,189
23,90990.8%

Visual interpretation (Pixels)
VacantTotal
36,202
57,922
49,83852,127
86,140110,049
58.0%
-

UA
37.5%
95.8%
65.1%

UA: user’s accuracy, PA: producer’s accuracy, SA: system accuracy

Table 8. Analysis results for developed districts.

Image
Analysis(Pixels)

Building
VacantTotal
PA

Building
89,057550
89,607
99.4%

Visual interpretation (Pixels)
VacantTotal
65,715
154,772
7,815
8,365
73,530163,137
10.6%
-

UA
57.5%
93.4%
59.4%

UA: user’s accuracy, PA: producer’s accuracy, SA: system accuracy

6. Conclusions

Vacant (%)
Buildings (%)

Fig. 16. Quantitative recovery process.

Pisco. In general, the issuance of the certificate by thegovernment triggered reconstruction activities by residents.

Journal of Disaster Research Vol.9 No.6, 2014

Pisco was the area most damaged by the 2007 Peru
earthquake. The purpose of this research was to explore
the usage of satellite imagery for monitoring post-disasterurban recovery processes, focusing on the urban changes
in Pisco between 2007 and 2011. To this end, the authors
conducted field surveys in the city in 2012 and 2013 andreviewed literature surveys. It was determined that building reconstruction peaked between 2008 and 2009.
Following an understanding the five-year recovery process, the authors compared the reconstruction conditions
by visual interpretation with those by satellite image analysis. An accuracy of 71.2% was achieved by the visual interpretation results in congested urban areas. Although post-earthquake urban recovery in Pisco was relatively low until 2008, the activity increased substantially1067

Hoshi, T. et al.

in 2009. This tendency was likely affected by the issuanceof certificates by the government to prove legal residencein Pisco.
The research results also demonstrates that satellite imagery can be a useful tool in the monitoring and understanding of post-disaster urban recovery processes inareas in which long-term field surveys are difficult to
conduct because of temporal/economic costs and heavy
workloads. However, satellite images need to be carefully
selected by considering such parameters as observation
angle.

05092011 presentacion comision de vivienda.pdf, 2011 [accessed
Oct. 31, 2012][12] Resumen Ejecutivo Desarrollo Urbano de la Ciudad de Pisco, 2008.
[13] Municipalidad Provincial de Pisco, http://www.munipisco.gob.pe/
p web/index.php [accessed Dec. 5, 2013][14] Y. Murakami and S. Yamashita, “The Evaluation of Resolution Accuracy and Application for Remote Sensing Technique to Identify
Slope Failure Distribution Using GIS,” Advances in River Engineering, Vol.11, pp. 151-156, 2006 (in Japanese).
[15] REDD Research and Development Center, “Grasp Method ofForest Area Using Remote Sensing,” REDD + Reducing Emission from Deforestation and Forest Degradation-plus, 2012
(in Japanese), http://www.ffpri.affrc.go.jp/redd-rdc/ja/reference/03/
201212 applied2 chap03.pdf [accessed Dec. 6, 2013][16] S. Koshimura, S. Kayaba, and H. Gokon, “Object-based Image
Analysis of Post-tsunami High-resolution Satellite Images for Mapping the Impact of Tsunami Disaster,” IGARSS 2011, pp. 19931996, 2011.

Acknowledgements
This research was supported by Science and Technology ResearchPartnership for Sustainable Development (SATREPS) for “Enhancement of Earthquake and Tsunami Disaster Mitigation Technology in Peru.” The authors are grateful for the assistance of Peruvian government and residents in Pisco. In addition, the authors would like to acknowledge the contributions of Mr. Jorge
Morales, Prof. Shunichi Koshimura, and Dr. Hideomi Gokon at
Tohoku University, Prof. Masashi Matsuoka at Tokyo Institute ofTechnology, and Prof. Shun Watanabe at University of Tsukuba.

Name:
Tomoyo Hoshi

Affiliation:
Shimizu Corporation

References:[1] O. Murao and H. Nakazato, “Recovery curves for housing reconstruction in Sri Lanka after the 2004 Indian Ocean Tsunami,” Journal of Earthquake and Tsunami, Vol.4, No.2, pp. 51-60, 2010.
[2] O. Murao, K. Sugiyasu, and H. Nakazato, “Study on Recovery
Curves for Housing Reconstruction in Sri Lanka, Thailand, and Indonesia after the 2004 Indian Ocean Tsunami,” Proc. of the 10th Int.
Symposium on New Technologies for Urban Safety of Mega Cities
in Asia (USB), 8p., Chiang Mai, Thailand, 2011.
[3] M. Koarai, H. Sato, H. Une, and K. Amano, “Interpretation of geological hazard using high-resolution optical satellite imagery: Comparison of interpretation characteristics of satellite images,” Journalof the Geological Society of Japan, Vol.114, No.12, pp. 632-647,
2008 (in Japanese).
[4] S. Matsuzaki, F. Yamazaki, M. Estrada, and C. Zavala, “Visual
Damage Interpretation of Buildings Using QuickBird Images Following the 2007 Peru Earthquake,” the 3rd Asia Conf. on Earthquake Engineering, Bangkok, Thailand, 8p, 2010.
[5] Y. Nakaoka and K. Nakao, “The Investigation of the Damage Situation and the Revival Situation of the Great Hanshin Awaji Earthquake disaster by the Analysis of the Satellite Remotely Sensed
Image,” Research Memoirs of the Kobe Technical College, Vol.37,
pp. 81-86, 1998 (in Japanese).
[6] M. Higashida, N. Maki, and H. Hayashi, “Development of Recovery Process Observation System Using CCD Camera after a Disaster,” Journal of Social Safety Science, No.3, pp. 95-100, 2001 (inJapanese).
[7] Instituto Nacional de Defensa Civil (INDECI), Censo INEI en
las Zonas Afectadas por el Sismo del 15 Ago 07, http://www.
inei.gob.pe [accessed July 3, 2012][8] Earthquake Engineering Research Institute (EERI), “Learningfrom Earthquakes The Pisco, Peru, Earthquake of August 15,
2007,” EERI Special Earthquake Report – October 2007, pp. 1-12,
https://www.eeri.org/lfe/pdf/peru pisco eeri preliminary reconnai
ssance.pdf [accessed July 6, 2012][9] 2007 Pisco, Peru Earthquake Reconnaissance Team by Japan Society of Civil Engineers (JSCE), Japan Association for Earthquake
Engineering (JAEE) and University of Tokyo with the Collaborationof CISMID, National University Engineering, “A Reconnaissance
Report on the Pisco, Peru Earthquake of August 15, 2007,” pp. 1109, http://shake.iis.u-tokyo.ac.jp/Peru2007/JSCE JAEE Report/[accessed July 11, 2012][10] O. Murao, T. Hoshi, M. Estrada, K. Sugiyasu, M. Matsuoka, and F.
Yamazaki, “Urban Recovery Process in Pisco after the 2007 Peru
Earthquake,” Journal of Disaster Research, Vol.8, No.2, pp. 356364, 2013.
[11] Ministro de Vivienda, Construcción y Saneamiento, Peru, “Presentación del Ministro de Vivienda, Construcción y Saneamiento,”
http://www.vivienda.gob.pe/popup/Documentos presentaciones/10

1068

Address:
2-16-1 Kyobashi, Chuo-ku, Tokyo 104-8370, Japan

Brief Career:2012 Graduated from the College of Policy and Planning Sciences,
University of Tsukuba
2014 Graduated from Graduate School of Social Systems Engineering,
University of Tsukuba
2014- Shimizu Corporation

Selected Publications:• T. Hoshi and O. Murao, “The Recovery Plan and Planning of theDamaged Areas due to the 2011 Great East Japan Earthquake,” Proc. of
the Great East Japan Earthquake Workshop Series 2012 in Iwaki,
pp. 67-70, 2012.
• O. Murao, T. Hoshi, M. Estrada, K. Sugiyasu, M. Matsuoka, and F.
Yamazaki, “Urban Recovery Process in Pisco after the 2007 Peru
Earthquake,” Journal of Disaster Research, Vol.8, No.2, pp. 356-364, 2013.

Journal of Disaster Research Vol.9 No.6, 2014