Paper No. TS-4-4

ADVANCES IN THE DEVELOPMENT OF GIS TOOLS FOR THE
EVALUATION OF SEISMIC RISK
Miguel Estrada1) Associate Professor, Faculty of Civil Engineering, National University of Engineering, Peru
estrada@uni.edu.pe

Abstract: To evaluate the seismic vulnerability of an urban area it is necessary to know the type of buildings or theactual land use over it. The development of building inventory is costly and time consuming, therefore, in Peru only few
municipalities may have the capacity to develop such a kind of work. In this paper it is discussed the use of highresolution images to infer the type of social class and consequently the type of house or building typified by its major
characteristics, based on two characteristics: the number of pools and the concentration of vegetation that exist in eachblock of the area under study.

1. INTRODUCTION
2. UMBRELLA PROJECT
Peru is located in a seismic prone area and thedevelopment of the cities are so fast and do not pay attention
to the regulation on building constructions, so it is of interest
to study the type of building on urban area to know theirvulnerability in case of an extreme earthquake. One of themain data source to assess the type and condition of thebuilding are the census or inventory data that is developed
for the local governments. In most of the municipalities in
Peru there is not a policy to develop this kind of data, notonly because of lack of economic resources but also because
the absence of technical people that can understand theproblem and conduct this work. Therefore it is necessary
to develop a methodology to carry out a fast inventory ofbuildings and to recognize their vulnerability against
earthquakes. Recently high resolution imagery is becoming
more accessible, not only because price, but also because
multiple satellites are observing the earth surface constantly
given a rich source on information about the land use.
Lima city is characterized by people of different social
classes; these social classes may indicate the type of building
where they live, in terms of quality and if they followed theregulations during the construction of the building. Low
class people generally they construct their houses bythemselves, as they can and without following theregulations of the National Building Code.
In this study it is proposed to used high resolution
satellite imagery to recognize the social class of the people
by identify some characteristics of their houses like pools
and greenness and thus to classify the vulnerability of theurban area. Finally the land information is integrated with
PRISM sensor data to identify areas that are built overimportant slope to evaluate also hazard of these area.

This research is under the project “Enhancement ofEarthquake and Tsunami Disaster Mitigation Technology inPeru” the purpose of this project is to developed
technologies and measures for prediction and mitigation ofearthquake/tsunami disasters caused by large-magnitudeinter-plate earthquakes occurring off the coast of Peru.
This project is divided into five active groups. Group
1 will research on seismology and geotechnical issues, andgive the final response of the soils. Group 2, will study thesource, effects and countermeasures of tsunamis produced
by an earthquake. Group 3, will identify the most typicalbuilding problems and will propose retrofitting methods orwill give some advises to improve the actual building code.
Group 4, will develop methodologies to identify the land use,by using the satellite imagery and integrating all theinformation in a GIS platform to assess the risk of thebuilding as well as to develop a methodology for fast
assessment of the damaged area by an earthquake or tsunami.
Finally, Group 5 will take the entire group’s output and will
disseminate this knowledge and propose policies for asustainable development of the population. This project isunder the support of Japan International Cooperation Agency,
Japan Science and Technology Agency, Peruvian
International Cooperation Agency. And the institutions
directly involved in this study are the Chiba University andTokyo Institute of Technology, from the Japanese side andthe Japan Peru Center for Earthquake Engineering Researchand Disaster Mitigation, National University of Engineering,from the Peru side.

The area of study is the district of La Molina in Lima
Peru. This district has a population of approximately
130000 habitants. This district is very special in Lima
Metropolitan area, because here it can be found people ofdifferent social classes and therefore different construction
type and building quality. Anothernother issue in this district isthat it is growing very fast and the new developed urban area
is taking place on terrain with important slope. Because
soil conditions, buildings in this district have suffered ofimportant damage in past earthquakes, therefore
therit isimportant to know the type of building that are built on thisarea. In Figure 1 it can be seen the location and extensionof La Molina district, that is located in the middle east ofLima. Although this is a district with relatively good
economicalomical resources, the situation of the cadastral
information is still in the beginning stage that is why it isnecessary to use another source of information to evaluate
and assess the type of buildings.

As we can see in Table 1 the number of multispectral
bands that are included in one image is good enough todetect many materials over the earth surface. Thisadvantage is taken as the base for the analysis. In thisthi
research Worldview 2 images were selected instead ofIkonos (Cook et. al., 2000) images because two reasons:first: there is a better resolution 0.50 m vs. 0.60 m, andsecond spectral resolution in case of Worldview 2 has moresensibility as it can be seen in Figure 2.
Worldview 2 - Relative Spectral Radiance Response
1.00.90.8

Relative Response

3. STUDY AREA

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0350450550650750850950

1050

Wavelength (nm)Band 01 (Coastal)Band 04 (Yellow)
Band 07 (NIR 1)

Band 02 (Blue)
Band 05 (Red)Band 08 (NIR 2)

Band 03 (Green)Band 06 (Red edge)
Band Panchro

Ikonos - Spectral Radiance Response

Relative Spectral Response

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0350450550650750850950

Wavelength (nm)Band 1Figure 1. District of La Molina in Lima, Peru.
4. DATA CHARACTERISTICS
The date used in this work is the multispectral
Worldview 2 which major characteristics are shown in Table
1.
Table 1. Worldview
iew 2 scene characteristics.
Characteristic
Value
Acquisition date
March 11, 2010
Number of bands (M – P)
8–1
Rows/Columns (M – P)
4031/5463 – 16124/21852
Bits per pixel
16
Spatial resolution
2.0 m MS/0.5
0.5 m PAN
Wavelength (nm):Coastal (Band 1) 427 nm
Blue (Band 2)
478 nmGreen (Band 3)
546 nmYellow (Band 4) 608 nm
Red (Band 5)659 nmRed edge (Band 6) 724 nm
NIR 1 (Band 7)
831 nmNIR 2 (Band 8)
908 nmBand 2

Band 3

Band 4

Figure 2. Spectral response comparison between
Worldview 2 and Ikonos images.
images
5. METHODS AND PROCEDURES
To assess the seismic vulnerability of an urban area canbe done by evaluating of housing census data, but it has thedrawback that that method cannot be generalized, one
approach based on contextual analysis of image and GIS
data is presented by Ebert et al (2008) where ana approachbased on proxy variables that were derived from highresolution optical and laser scanning data was applied, incombination with elevation information and existing hazard
data social class we investigate the location of swimming
pool within an urban area. Another approach for building
inventory is presented by Dutta et. al (2005) where very highresolution satellite optical data from Quickbird satellite with
object-orientedoriented classification scheme was used for an overall
accuracy of 85% in detailed classification of urban land
cover.

900800
700
600500DN

In La Molina district it can be found different types ofland use depending on the social classes. As it is shown in
Figure 3, high social class area (a) has an important green
area and important features are the pools inside of the lot,middle class area (b) has also important green area but nopools, and low class area has few vegetation and no pools.
Therefore if pools and vegetation can be detected in an
automatic manner we could infer the type of social class inthe area and as consequence the social vulnerability of it.

4003002001000Band 1Band 2Band 3

Band 4

MaxMeanBand 5

Band 6-1 Std+1 StdBand 7Band 8BandsMin

Figure 5. Trained pixels and their spectral response.
Another parameter that was used is the NDVI to detect
the concentration of vegetation in the area.
6. RESULTS

(a)

(b)

(c)

Figure 3. Difference between land use in La Molina.
For mage classification exits a bunch of methods,
mainly classified into two big groups: unsupervised andsupervised methods. One of the methods of supervisedclassification is described by Mohd et. al. (2007) theSpectral Angle Mapper (SAM) this is a physically-based
spectral classification that uses an n-dimensional angle tomatch pixels to training pixel area. The algorithm determines
the spectral similarity between two spectra by calculating theangle between them, treating them as vectors in a space withaxes equal to the number of bands. Smaller angles represent
closer matches to the training pixels.

The SAM method for classification gave good results
and the pools could be detected, the number of pools in one
block where automatically counted and the image wastransferred to GIS map and then classified by this number.
Additionally, the NDVI also was calculated and thenumber of pixels that represent vegetation where counted ineach block.
Figure 6 shows the thematic map considering thenumber of pools by block, it can be seen that there are someareas where the number of pools is considerable given areasof high class. In Figure 7, is presented the thematic map
representing the concentration of vegetation in each block,high concentration of vegetation indicates high class, whilevery low concentration may indicate low class.

Luc et. al. (2005) depicts how to obtain the angle
between two spectra that are being compared. In Figure
4 it can be seen, in a three bands dimension space, if spectral
angle (θ) is small then the tested (t) and reference (r) spectral
vectors are similar. In ¡Error! No se encuentra elorigen de la referencia., we can see the numericalcalculation, where the angle θ is a kind of correlation value
between two samples in an n-dimensional spectral space.
¡Error! No se encuentra el origen de la referencia.

Figure 6. Classified map indicating the number ofpools by block.
Figure 4. r reference spectra vector, t spectra vector
to be compared, θ spectral angle .
Pixels that are in pools are selected as trained pixels.
Pixels were selected over the image, and their spectral
response was analyzed. Figure 5, shows how the pixels
were selected and the average of the digital number of thesepixels.

the houses may have not follow the standards and therefore
their seismic vulnerability could be considerable.

Acknowledgements:
The authors would like to thank to the Japan Science andTechnology Agency and Japan International Cooperation Agency
for the support in acquiring the satellite data and the field survey..

Figure 7. Classified map indicating the number ofvegetation pixels by block.
Also it was conducted a field survey for verification of
the results given good agreement with the two indexes. In
Figure 8, it can be seen that for different type of land use it ispossible to correlate the number of pools and the NVDI.

Figure 8. Field survey of different sectors.
3. CONCLUSIONS
It can be shown that from satellite information it can beinferred the type of social class based on two characteristicsof the urban area: the number of pools and the concentrationof vegetation. While the number of pools and theconcentration of vegetation is high it is and indicative thatthe social class of the people who live there is high andtherefore their houses may be well constructed, while on theother side where pools do not exist and the vegetated area isminor may indicate a social class where the construction ofReferences:
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