Earthquake damage assessmentusing satellite data.

Waheed Ahmad Khan1, Fabio DELL’ACQUA2, Diego Aldo POLLI2,1National Engineering Services (NESPAK) Pvt. Ltd. Lahore, Pakistan

2IUSS (Istituto Universitario Studi Superiori), I-27100 Pavia ITALY

Abstract. Earthquake disaster, as well as other natural disasters, represents the challenges for earth observation systems to demonstrate their usefulness in supporting intervention and relief actions. Undertaking damage notification and assessment for large areas is very expensive; consequently for emergency intervention, it is important to identify the damaged areas. Automated earthquake damage assessment from post-event only remotely sensed data is highly desirable, especially when new generation, Very High Resolution (VHR) spacing Radar data is concerned, lacking extensive pre-event archives. Though, most damage assessment method either rely on human interpretation or on pre-post even comparison. In this research work, studies on the 6th April 2009 earthquake event, which stroke L’Aquila, Italy and on the 12th January 2010 earthquake event, Port-au-Prince, Haiti are considered. The objective of this study is to develop an improved method to assess the earthquake damage by the use of very high resolution images and make some actual texture damage correlations, and to determine whether it was effective to change the partitioning strategy from the previous works.

Keywords: Very High Resolution (VHR), Synthetic Aperture Radar (SAR), seismic damage assessment, post-event only.

Introduction

A natural disaster is an event caused by natural forces of nature that often has a significant effect on human beings socially as well as financially. Typically the human populations either are displaced (left homeless) or killed as a result of natural disaster. Earthquake is one of the natural and also dangerous disasters. Earthquake motion is caused by the quick release of stored potential energy and its conversion into kinetic energy of motion. Most earthquakes are occurring along the faults, tectonic plate boundary zones, or long the mid-oceanic ridges. The prevention of earthquakes is impossible with today’s knowledge and technologies, but by utilization of advanced techniques, minimizing the losses as a result of earthquake and quick rehabilitation and quick damage assessment is possible. For detecting the changes as a result of earthquake is possible by using the remote sensing technique, Image rationing and image differencing are the frequently used methods in change detection applied in post-Earthquake damage assessment studies. There are also several other techniques such as digital image classification and texture analysis to detect the damage caused by an earthquake. Besides,

various kinds of data sources are available to be used in the post-earthquake damage assessment studies, which are night time imagery, optical imagery, radar imagery, aerial photography, aerial video imagery and airborne MSS imagery.Satellite remote sensing can be very useful instrument to help the decision chain of the civil protection authorities after the earthquake.

Rapid impact assessment after a catastrophic event is crucial for initiating effective emergency response actions. Remote sensing satellites equipped with very high spatial resolution (VHR) optical and synthetic aperture radar (SAR) imaging sensors can provide important information about the affected areas since they can map the regions of interest quickly, with a high geometric precision, and in an uncensored manner.

Objective

The aim of this work is to develop an improved method to assess the earthquake damage by the use of very high resolution images and make some actual texture damage correlations, and to determine whether it was effective to change the partitioning strategy from the previous works. This approach is implemented in a selected urban area of the Italy, L’Aquila, which was one of the urban areas most strongly affected in Earthquake on 6th April 2009.

Methodology

The first step in this method was to decide the criteria on the basis to assess the damage by the earthquake by the remote sensing technique. We had different option on the basis of which we could classify the observing area like;

1. Type of Building (R.C.C or Masonry)2. Height of building (Number of Stories)3. Type of roofing

Option of dividing the area on the basis of height of building was selected because there were some ambiguities in classifying the area on the basis of type of building like we cannot be sure about the type of building as from the images it cannot be possible to detect whether it is reinforced building or masonry building or confined masonry building or composite building.

Classifying the area on the basis of number of stories (number of slabs) by looking through the Google earth tool to all the possible views needed to classify the area on the basis of number of stories, to make the end result reliable. Vacant spaces are not included while dividing the area (roads, parks, unconstructed areas) to make the result more accurate. There are some areas where the buildings stories are not exactly equal (just few were of different height), those area are classified on the basis of majority of building stories. There are also some parts in cities where buildings are of different heights, and it was not possible to divide the area on the basis of one building classification, therefore these area are did by seeing that what was the minimum height of building in that area of most of the buildings, and then divide that area by saying that in this area all the buildings are equal or higher then this

number of stories. Different polygons have given different colour to differentiate among the areas representing the height of building as shown in below figure. Different coloured sub divided area shows the different height areas, like red area means the three storey buildings area and so on.

Correlations

SAR images are affected by speckle noise, so a good technique to extract information is the use of texture measures. In fact with this method we do not consider the value of single pixel, but the response of groups of pixels; moreover texture measures are believed to reflect to some extent the difference in expected distribution of backscattered intensities between orderly non damaged buildings and scrambled, heavily damaged buildings. The correlation between the specific number of stories and the damaged area was developed and different combinations were made to find out the best correlation.

Two approaches were used to develop some correlation between the damaged areas with respect to building height for the L’Aquila city explained below.

First Approach

In the first approach, the correlation between the specific number of stories and the damaged area was developed and different combinations were made to find out the best correlation and the result as shown in the Table 1 and Table 2.

Texture without with classification(number of stories)

Figure 1-Partition of City L’Aquila into Blocks

Values classification

1,2 3,4,5 1,2,3 4,5 1,2,3,4 5 1,2,3,4,5 6,7

Correlation -0.30 -0.41 -0.26 -0.46 0.06 -0.29 -0.13 -0.30 -0.13

Contrast 0.27 0.41 0.22 0.42 -0.06 0.27 0.11 0.27 0.11

Dissimilarity 0.31 0.45 0.26 0.46 -0.06 0.31 0.13 0.31 0.13

Entropy 0.37 0.46 0.33 0.52 -0.01 0.35 0.20 0.37 0.20Homogeneit

y -0.33 -0.43 -0.29 -0.44 0.02 -0.32 -0.12 -0.33 -0.12

Mean 0.23 0.18 0.23 0.23 0.05 0.19 0.25 0.23 0.25Second

Moment -0.37 -0.46 -0.33 -0.53 0.00 -0.36 -0.19 -0.37 -0.19

Variance 0.23 0.38 0.19 0.32 -0.08 0.20 0.05 0.22 0.05Table 1 Correlation between texture measures and DAR of different storey buildings

(19 angle view)⁰

without classification

with classification(number of stories) 1,2 3,4,5 1,2,3 4,5 1,2,3,4 5 1,2,3,4,5 6,7

Correlation -0.12 -0.13 -0.14 -0.16 -0.17 -0.16 0.08 -0.10 0.08

Contrast 0.04 0.00 0.07 0.02 0.14 0.03 -0.14 0.02 -0.14

Dissimilarity 0.08 0.04 0.10 0.08 0.15 0.07 -0.09 0.06 -0.09

Entropy 0.11 0.05 0.13 0.09 0.16 0.12 -0.06 0.09 -0.06

Homogeneity -0.13 -0.13 -0.13 -0.17 -0.11 -0.12 -0.02 -0.11 -0.02

Mean -0.18 -0.22 -0.16 -0.19 0.02 -0.13 -0.17 -0.18 -0.17Second

Moment -0.16 -0.12 -0.17 -0.13 -0.17 -0.16 0.00 -0.14 0.00

Variance -0.01 -0.13 0.04 -0.06 0.20 0.07 -0.20 -0.01 -0.20

Table 2 Correlation between texture measures and DAR of different storey buildings (50 angle view)⁰

As shown in Table 1 and Table 2, the strong correlation between the texture values and DAR exists when the view angle is 19º. And the strongest correlation exists for the Entropy among the texture values for all the combination of stories.

In Table 1, it can be seen that results are not linearly changing with respect to the number of stories, the values are scattered though it gives higher correlation value with the classification

Second Approach for L’Aquila

In this approach, effort was made to develop some relationship of earthquake damage with the number of storeys of building which was our assumption of research.

Total area was divided on the basis of number of stories in the city as shown in figure 1. As the area calculated by analysing the polygons on GIS, area of each polygon was obtained and calculated with respect to the particular classification as shown in the Table 3. To make the correlation between the number of stories and the damage, percentage of each area with respect to total area was calculated and in similar way the damaged to the particular area with respect to the total area was calculated as shown in Table 3.

No. of Stories Area(A) Percentage of total area (%)=(100*A/B)

Damaged portion (%)=C=(damaged

area/B)

Damage on basis of building height (%)=(100*C/D)

storey 1 206286 11.75 0.38 5.48

storey 2 303586 17.30 1.21 17.55

storey 3 562034 32.02 4.59 66.57

storey 4 438114 24.96 0.64 9.32

storey 5 245053 13.96 0.07 1.08Total area of

observation(B)1755073 100.00 D=6.90 100.00

Table 2 Damaged area with respect to Total area of observation

After calculating the damaged area with respect to the total area of observation, correlation was developed between them (correlation value was 0.834). As can be seen in Table 3 that area is increasing up to 3 storeys and then it decreases and again increases. Its clear from the result that the damaged area is directly proportional to the area and not dependent on the area of classification.

CONCLUSION

Two approaches were used to assess earthquake damage from post event images with respect to the building heights in L’Aquila city. In the first approach, our aim was to test whether a correlation between texture and damage could be made stronger by restricting the entire sample set to specialized subsets (subsets were made on the basis of number of stories); e.g. is the texture-damage correlation stronger if we compute it only on 1-storey buildings? And by analysis we found that a very strong correlation is developed if we consider only subset of 1-storey.

While in the second approach relationship of earthquake damage with the number of storeys of building was developed.

From the results obtained using approach one, it seems clear that there is no relationship between the texture values corresponding to building height (There is no linear relationship, which can be seen in Table 1 and Table 2). But if we see the result we can found that if sample of buildings was restricted to 1-storey buildings only, we get a much higher correlation between one texture measure and the damage extent, than the other. By seeing the results it can be commented that if the sample is distributed uniformly we get better results in term of correlations rather than classification.

From the results of second approach, it can be said also that there is almost linear relationship between the sub area and the damaged area i-e if the area is increased under observation, damage portion is increased(as can be seen in Table 3), means that classification on the basis of storey height doesn’t make any affect.

The results obtained from the analysis seems to be understandable as its not only the building height which affects the damage quantity, other factors, like the distance from the fault line, soil properties, type of building, type of foundation are also the important factors in assessing the damaged area . And all these factors vary, so therefore the damage quantity. So it seems better to assess the earthquake damage without classification on the basis of number of storeys, because we get good correlation value in that case for the Entropy.

REFERENCES

Anne-Lise Chesnel and Renaud Binet “Urban damage assessment using multimodal QuickBird images and ancillary data: the Bam and the Boumerdes earthquakes” D´epartement Analyse et Surveillance de l'Environnement Bruy_eres-le-Chˆatel France, available online at:http://tlc.unipv.it/6_RSDMA/Finals/1.3%20-%20Chesnel.pdf.

Syed Jamil Hasan Kazmi “On The Use of Remote Sensing and GIS Techniques in PostEarthquake Damage Identification and Assessment in Pakistan” Department of Geography,University of Karachi, Pakistan, available online at: www.merci.ethz.ch/Workshop/Presentations/Kazmi_Paper.pdf

Shunsuke Otani Seismic “Vulnerability Assessment Methods for Buildings in Japan” Department of Architecture, Graduate School of Engineering, University of Tokyo Japan available online at www.ctsee.org.tw/pdf/ee0202/ee0202-5.pdf.

A report on Earthquake Vulnerability Reduction for Cities (EVRC-2) Module 3 Section 1(b), available online at:

http://www.adpc.net/casita/Course%20Modules/Earthquake%20vulnerability%20reduction%20for%20cities/EVRC0301A_Earthquake_Vulnerability.pdf.

Polli, Dell’Acqua, Gamba, : “Earthquake damage assessment from post-event only radar satellite data”, 8th / RSDMA/ workshop, Tokyo , 30th September-1st October 2010.