using high-resolution satellite images for post-earthquake building damage assessment: a study...

Using High-Resolution Satellite Images forPost-Earthquake Building DamageAssessment: A Study Following the 26January 2001 Gujarat EarthquakeKeiko Saito,a) Robin J. S. Spence,b) M.EERI, Christopher Going,c) andMichael Markusd)

Newly available optical satellite images with 1-m ground resolution suchas IKONOS mean that rapid postdisaster damage assessment might be madeover large areas. Such surveys could be of great value to emergency manage-ment and post-event recovery operations and have particular promise forearthquake areas, where damage distribution is often very uneven. In this pa-per three satellite images taken before and after the 26 January 2001 Gujaratearthquake were studied for damage assessment purposes. The images com-prised a post-earthquake cover of the city of Bhuj, which was close to theepicenter, and pre- and post-earthquake cover of the city Ahmedabad. The as-sessment data was then compared with damage surveys actually made on-site. Three separate experiments were conducted. In the first, the satellite im-age of Bhuj was compared with detailed ground photos of 28 severelydamaged buildings taken at about the same time as the satellite image, to in-vestigate the levels and types of damage that can and cannot be identified. Inthe second experiment, the whole city center of Bhuj was damage mappedusing only the satellite image. This was subsequently compared with a mapproduced from a building-by-building damage survey. In the third experi-ment, pre- and post-earthquake images for a large area of Ahmedabad werecompared and totally collapsed buildings were identified. These sites weresubsequently visited to confirm the accuracy of the observations. The experi-ment results indicate that rapid visual screening can identify areas of heavydamage and individual collapsed buildings, even when comparative coverdoes not exist. The need to develop a tool with direct application to supportemergency response is discussed. [DOI: 10.1193/1.1650865]

INTRODUCTION

Remote sensing may be defined as the art and science of obtaining information aboutan object without being in direct physical contact with it (Jensen 2000). Remote sensing

a) Research Associate, The Martin Centre for Architectural and Urban Studies, Department of Architecture, Uni-versity of Cambridge, 6 Chaucer Road, Cambridge CB2 2EB, United Kingdom

b) Professor in Architectural Engineering, Department of Architecture, University of Cambridge, 6 ChaucerRoad, Cambridge, CB2 2EB, United Kingdom

c) GeoInformation Historical Limited, Telford House, Cow Lane, Fulbourn, Cambridge CB1 5HB, United King-dom

d) Research Associate, Institute for Technology and Management in Construction, University of Karlsruhe,D-76128 Karlsruhe, Germany

145Earthquake Spectra, Volume 20, No. 1, pages 145–169, February 2004; © 2004, Earthquake Engineering Research Institute

146 K. SAITO, R. J. S. SPENCE, C. GOING, AND M. MARKUS

sensors onboard satellites or airplanes are a good way to capture damage information ofareas struck by an earthquake, since they operate from a high altitude and are not sub-jected to the chaotic situation in the immediate aftermath of an earthquake. Although theapplication of image analysis techniques to post-earthquake damage assessment is not anew idea, it is only recently, following the launch of three commercially operated satel-lites, namely, IKONOS-2, EROS A1, and QuickBird-2, capable of taking very high reso-lution images, that it has become possible to consider carrying out a damage survey atthe level of individual buildings using satellite images.

Building damage is the primary cause of human casualties, and the collapse of alarge building can result in a significant number of deaths (Coburn and Spence 2002).Thus, the ability to make building damage assessments available rapidly could signifi-cantly improve immediate postdisaster emergency operations. Remote sensing can alsocontribute to the mapping of other damage data at various stages after the disaster.

IKONOS, EROS, and QuickBird images can be categorized as optical images, sincethey are taken by optical sensors. Optical sensors are designed so that they can recordthe reflectance values of the ‘‘visible region,’’ namely, wavelengths between 0.4 to 0.7mm, of the electromagnetic spectrum reflected or emitted from objects on earth’s surface(Lillesand and Keefer 1994). This is called the ‘‘visible region’’ since it is the region ofthe electromagnetic spectrum, to which human eyes are most sensitive. Characteristicsof the three satellites are listed in Table 1.

Image analysis techniques applied in previous studies involving post-earthquakedamage assessment can be categorized into two groups: quantitative analysis and quali-tative analysis. The former can be described as analysis that uses algorithms, and thelatter as analysis that relies on human vision and the mechanism of the human brain torecognize and understand objects. Obviously, the results of both types of image analysisstudies will always be dictated by the quality and characteristics of the image used.Therefore it is important to consider the objective of the analysis and use an image thatis appropriate for the purpose of the study.

Previous image analysis studies carried out using optical satellite images of lowerspatial resolution were limited to producing area-based damage assessment, as in thecase of the change detection study using pre- and post-multispectral Landsat 7 images ofAnjar, Gujarat, India (Yusuf et al. 2001) with 30-m spatial resolution. Huyck et al.(2002) carried out automatic change detection studies using 20-m resolution pre- andpost-earthquake SPOT images of Golcuk, Turkey. This study again demonstrated thatsatellite images with this level of spatial resolution can be used to detect the most dam-aged areas after an earthquake. Hasegawa et al. (1999) demonstrated that damages tobuildings can be visually identified to some extent by using high-resolution TV imagestaken after an earthquake. Since very high resolution optical satellite images have be-come available, two studies using qualitative analysis on IKONOS images have beencarried out to undertake post-earthquake damage assessment in the town of Bhuj, Gu-jarat, India (NPA 2001 and Chiroiu et al. 2002). The Mw57.7 earthquake in January2001 caused great damage to its buildings and infrastructure. Both studies have demon-strated that very high resolution optical satellite images have the capability of showingindividual severely damaged buildings. However, these studies did not attempt to carry

USING SATELLITE IMAGES TO ASSESS BUILDING DAMAGE FOLLOWING THE 2001 GUJARAT EARTHQUAKE 147

Table 1. Orbital characteristics of IKONOS-2, EROS A1 and Quick Bird (Quoted from SpaceImaging Inc. 2003, ImageSat Inc. 2003 and Digital Globe 2003)

Satellite IKONOS-2 EROS A1 QuickBird-2

Operated by SpaceImaging Inc. CO,USA

ImageSat International,Cyprus

DigitalGlobe, CO, USA

Launch date September 1999 December 2000 October 2001

Altitude 423 miles/681 km 480 km 450 km

Period of orbit(min)

98 minutes 94–96 minutes 93.4 minutes

Inclination 98.1 degrees 98 degrees

Orbit type Near polar, sunsynchronous

Sun synchronous, polarorbit

Near polar, sunsynchronous

Revisit period 2.9 days at 1-meterresolution; 1.5 days at1.5-meter resolution.These values are fortargets at 40 degreeslatitude. The revisittimes will be morefrequent for higherlatitudes and lessfrequent for latitudescloser to the equator.

Oblique viewing up to45 degrees enables anysite on earth to beviewed two to threetimes a week.

1–3.5 days, dependingon latitude

Look angle Agile spacecraft-in-track and cross-trackpointing

Agile spacecraft-in-track and cross-trackpointing

Agile spacecraft-in-track and cross-trackpointing

Spatialresolution atnadir (GroundSample Distance)

Panchromatic: 1 mMultispectral: 4 m

Panchromatic: 1.8 m Panchromatic: 0.6 mMultispectral: 2.44 m

Swath width atnadir

11 km (nominal) 13.5 km (nominal) 16.5 km (nominal)

Sensor resolution Panchromatic: 445–900mmMultispectral:Red 450–520 mmGreen 510–600 mmBlue 630–700 mmNear Infrared 760–850mm

Panchromatic: 500–900mm

Panchromatic: 450–900mmMultispectral:Red 450–520 mmGreen 520–600 mmBlue 630–690 mmNear Infrared 760–900mm

Dynamic range(bits per pixel)

11 bit or 8 bit 11 bit 11 bit


out a systematic approach in their damage assessment, but had only spotted several ofthe collapsed buildings. Neither study used ground survey data to verify their findings.

Chiroiu et al. (2002) also demonstrated the limits of conventional change detectionalgorithms using IKONOS images. The limitation comes from the fact that these con-ventional methods assume that the images to be compared for change detection are bothtaken from nadir, the point vertically above the center of an image. The look angle ofany two IKONOS images can vary, which results in producing lots of ‘‘noise’’ not relatedto change on the ground but resulting from the difference in the look angle when con-ventional change detection technique is applied. Texture analysis has also been appliedon high-resolution panchromatic TV images taken from a helicopter from an obliqueangle, immediately after the Kobe earthquake in 1995 (Mitomi et al. 2002). The resultsyielded estimates of very small areas with building damage and were generally in rea-sonably good agreement with the damage that was identified on the ground. Imagestaken by sensors such as radar sensors have also been used in the context of post-earthquake damage assessment (Matsuoka and Yamazaki 2002). Radar images recordthe amplitude and phase information of radar signals sent out from and reflected back tothe sensor. However, to date, the spatial resolution of these images is still limited to10–20 m. Although radar images have the huge advantage, compared with optical im-ages, that the signals will not be affected by cloud cover, they cannot at the current spa-tial resolution be used for a building-by-building damage assessment. Other promisingremote sensing techniques that could eventually be applied in this area are airborne laserscanning (Steinle et al. 2001) and height analysis using airborne SAR (Eguchi et al.2000). However, none of these techniques have reached the stage where they can be ap-plied in a practical situation.

The main objective of this study was to examine, in detail, the usefulness of veryhigh resolution satellite images for post-earthquake building damage assessment focus-ing on the use of visual interpretation. Visual interpretation was chosen as the method ofcarrying out damage assessment in this study since it can be said that visual interpreta-tion still holds an important role in the application of remote sensing. Visual interpreta-tion is defined as interpretation of images based on the feature’s tone, pattern, shape,size, shadow, texture, and association (Prasad and Sinha 2002). Unfortunately, this arthas not been taught systematically in the last 20 years (Jensen 2000, Prasad and Sinha2002), and as a result is in danger of being lost (Estes and Jensen 1998). According toone study, a landcover analysis carried out by applying algorithms to satellite images canonly achieve 70 to 80% accuracy (Jensen 1996), even when as few as five categories oflandcover types are being classified. It is therefore likely that, for the foreseeable future,the results of quantitative analysis will have to be complemented by visual interpreta-tion. Therefore there is a case for focusing on visual interpretation techniques and im-proving their effectiveness. In this study on-screen visual interpretation was carried out,which provides the interpreter the opportunity to derive more information from the im-age in some cases, compared to conventional photo interpretation, by changing the con-trast, brightness of the images.

Two cities, namely, Bhuj and Ahmedabad, Gujarat, northwest India, were chosen tocarry out post-earthquake building damage assessment using very high resolution satel-lite images (Figure 1). Both cities suffered damage from the Gujarat earthquake on 26


January 2001. This Mw57.7 earthquake, India’s most severe in more than 60 years,caused damage in more than 20 districts in Gujarat Province, and was devastating inKutch District, where more than 12,000 people died. Its effects are described in detail inan EERI reconnaissance report (Jain et al. 2002). The Gujarat earthquake was one of thefirst major earthquakes covered by IKONOS.

Three IKONOS images were acquired for damage analysis. No pre-earthquake coverwas available for Bhuj and the image of the town used was taken a week after the earth-quake. The other two are pre- and post-earthquake covers of Ahmedabad, taken approxi-mately 2 months before the earthquake and the day after the earthquake, respectively.Table 2 shows the details of the images acquired. The images chosen are panchromaticimages, since these have the highest spatial resolution, i.e., 1 m. Panchromatic imagesare optical grayscale images with a single band. Multispectral images are also availablethat use three bands to represent the red, blue, and green regions within the visible re-gion of the electromagnetic spectrum. Most of the previous optical studies have usedmultispectral images, but these have a lower spatial resolution.

The three images acquired are all GEO products. These are rectified to a map pro-jection system and a specific plane parallel to the Earth ellipsoid. Image distortions in-troduced by the collection geometry were rectified. Both rectifications are carried out by

Figure 1. Map of India showing the two towns Bhuj and Ahmedabad in Gujarat, northwestIndia. (Image source: http://www.mapsofindia.com)


the image supplier, so the client receives the already georectified imagery. According tothe image supplier, GEO products are said to have Circular Error (CE) 15 m positionalaccuracy, exclusive of terrain effects, which means that location of objects in the imageare 90% of the time within this accuracy (Space Imaging 2003), provided that the studyarea does not contain much terrain relief. Since precise positional accuracy is not re-quired for the objective of this project, and also due to the lack of digital elevation modeland ground control points of the study area to orthorectify the image, the images wereused without any further rectification.

Table 2. Characteristics of the three images acquired for this study

Image Post-earthquakeimage of Bhuj

Pre-earthquake imageof Ahmedabad

Post-earthquakeimage of Ahmedabad

Experiment the imagewas used for

Experiment 1, 2 Experiment 3 Experiment 3

Date 2 February 2001 8 November 2000 27 January 2001

Time 06:05 am (GMT) 05:29 am (GMT) 05:47 am (GMT)

City covered Bhuj, Gujarat, India Ahmedabad (westhalf), Gujarat, India

Ahmedabad (westhalf), Gujarat, India

Area size 100 km2 121 km2 121 km2

Satellite IKONOS-2 IKONOS-2 IKONOS-2

Product type GEO (sensorcorrected, rectified toa plane parallel topre-definedellipsoid)

GEO (sensorcorrected, rectified toa plane parallel topre-definedellipsoid)

GEO (sensorcorrected, rectified toa plane parallel topre-definedellipsoid)

Spatial resolution 1 m 1 m 1 m

Type of imagery Panchromatic Panchromatic Panchromatic

Positional accuracy CE 15 m (excludingterrain effects)

CE 15 m (excludingterrain effects)

CE 15 m (excludingterrain effects)

Dynamic range 11 bit 11 bit 11 bit

Sun elevation angle 44.45977 degrees 45.41117 degrees 42.54 degrees

Look angle 69.19741 degrees 61.66112 degrees 67.0345 degrees

Map projection UTM 42N, WGS84 UTM 43N, WGS84 UTM 43N, WGS84

Resampling method Cubic Convolution Cubic Convolution Cubic Convolution

File format GeoTIFF GeoTIFF GeoTIFF


Three experiments were carried out using the images. The first was a comparisonstudy using ground photographs of the damaged buildings and the post-earthquake sat-ellite image of Bhuj. The authors had available photographs of 28 damaged buildingstaken at nearly the same time as the IKONOS image, and these were then used to assesswhat kind of building damage was most readily identifiable on the IKONOS images. Thesecond experiment was an area-based damage assessment of Bhuj. The study area in thepost-earthquake IKONOS image was divided into grids. Each grid square was assigneda damage level according to the percentage of the collapsed and severely damaged build-ings visible within a grid square, which resulted in a grid-based damage map of Bhuj.The third experiment used pre- and post-earthquake images of west Ahmedabad. Thewest part was chosen since some damage data from the west side of the river was avail-able, which was collected during the Earthquake Engineering Field Investigation Team(EEFIT) damage survey (Madabushi 2002) carried out shortly after the earthquake. Thesame grid was put on top of the two satellite images, then a grid-square-by-grid-squarevisual interpretation was carried out to identify collapsed buildings.

After the interpretation was done a field trip was conducted to verify the results ofthe experiments and assemble the immediate post-earthquake damage data. Area-basedground survey damage of Bhuj was made available through a nonprofit organization En-vironmental Planning Collaborative (EPC) that carried out damage surveys in four citiesin Gujarat. For Ahmedabad, the twelve buildings assessed as collapsed using the satelliteimages were visited by the authors to check the assessment. The following sections de-scribe the methodology and results obtained from the three experiments, and considerthe implications.

EXPERIMENT 1: COMPARISON STUDY USING GROUND PHOTOGRAPHSAND SATELLITE IMAGE

METHODOLOGY

In this first experiment, ground photographs of the damaged buildings in Bhuj andthe satellite image were compared to see whether completely collapsed buildings couldbe identified on a post-earthquake high-resolution satellite image. The experiment wasalso designed to assess the level of damage to buildings that can be seen using thesesatellite images. The ground photographs were taken as a part of an investigation by theUniversity of Karlsruhe into post-earthquake damage patterns; the post-earthquake con-dition of 28 buildings was recorded in great detail using a digital camera. The locationsof the buildings were recorded as waypoints using a handheld GPS system (single mea-surement). These photographs were taken on dates very close to the same day (2 Febru-ary 2001) as the satellite image used in this experiment, therefore constitute excellentground-truth data.

First, a damage level was assigned to the damaged buildings in the pictures, usingthe European Macroseismic (EMS) scale (European Seismological Commission 1998).After identifying the exact location of the buildings on the satellite image, comparison


was made between the damage visible in the ground photographs and the building in thesatellite image. Figure 2 shows an example.

RESULTS: VISIBLE DAMAGE

The results are summarized in Table 3. Visible damage types marked with a* arecases of damage that are visible with the aid of additional information such as groundphotos or pre- earthquake satellite image. In general, it can be said that damage sus-tained on high-rise buildings is easier to spot compared to that of low-rise buildings.Single buildings that had totally collapsed were clearly visible (Figure 3). Other visibletypes of damage were heavily tilted buildings (Figure 4), water tanks that had fallen offor been displaced (Figure 5), severe vertical splits in buildings (Figure 6) and overturned

Figure 2. Example of a comparison carried out in Experiment 1 between a ground photographand a satellite image. White arrow shows the look angle of the ground photo. The scale of thesatellite image is approximately 1:1,500. (Ground photo by M. Markus.)

Table 3. Summary of the results of Experiment 1. Visible damage types markedwith* are damages that are visible with the aid of additional information such asground photos or pre-earthquake satellite image

Damage visible on IKONOS image Damage not visible on IKONOS image

Totally collapsed single buildings Damages in built-up areas, even totallycollapsed buildings.

Heavily tilted buildings* Damages hidden in the shadowsWater tank displacement Damages on walls (cracks etc)Split in the middle of a high-rise building* Damages on low-rise buildingsTurned-over buildings* Lower-story collapseDebrisShadows (useful for height approximation)


buildings (visible with some difficulty) (Figure 7). Debris, as can be seen in Figure3,tends to show up on the satellite image as ‘‘speckles’’ of gray and white pixels. Theexistence of debris spreads extensive enough to be visible on the satellite image could beused to infer the existence of a nearby building that can be classified as at least damagelevel D4 (EMS scale).

Figure 3. Photo (a) and satellite image (b) of a collapsed single building in the north of Bhuj.The remains of the building are visible in the satellite image. Note the difference between it andthe building immediately to the left that is still standing. The white arrow in the satellite imageshows the look angle of the photo. The scale of the satellite image is approximately 1:1,500.(Ground photo by M. Markus.)

Figure 4. Photo (a) of a tilted building and satellite image (b) showing the same building. Notethe building on the corner of the street that does not line up with the building next to it. Scaleof the satellite image is approximately 1:1,500. (Ground photo by M. Markus.)


DISCUSSION

The results of the above experiment suggest that there are several types of buildingdamage (for example, titled buildings) that could be better identified with the help ofadditional information. This includes pre-earthquake imagery (satellite or aerial), build-ing height data, building inventory data, footprint data of buildings, and ground photos.

Figure 5. Photo (a) and satellite image (b) of a displaced water tank on rooftop. The whitearrow shows the look angle of the photo. The displaced water tank is clearly visible on the sat-ellite image. The scale of the satellite image is approximately 1:1,500. (Ground photo by M.Markus.)

Figure 6. Two buildings in between which the gap has widened, as seen in the photo (a) andalso visible in the satellite image (b). White arrow depicts the look angle of the photo. Scale ofthe satellite image is approximately 1:1,500. (Ground photo by M. Markus.)


Building height data would be especially useful to identify lower-story collapse,which can be almost impossible to identify using visual interpretation alone. Since thefootprint of these buildings will remain almost the same post-collapse, the damagedbuilding will look normal from above (Figure 8). One of the most obvious changes on abuilding resulting from lower-story collapse would be the change in its height. If the

Figure 7. Photo (a) of a building that has fallen over. In the satellite image (b), blackrectangular-shaped objects, which presumably are windows or doors, are visible on the side ofthe building facing the sky. This type of damage will be difficult to identify without the help ofground photos. White arrow shows the look angle of the photo. Scale of the satellite image isapproximately 1:1,500. (Ground photo by M. Markus.)

Figure 8. Photo (a) of a building with collapsed ground and first floors. The same building iscircled in white in the satellite image (b), although the lower-story collapse is not visible. Scaleof the satellite image is approximately 1:1,500. (Ground photo by M. Markus.)


building height can be measured on both the pre- and post-earthquake images, it shouldbe possible to measure the difference in height, which will enable assessment of whetherlower-story collapse has occurred.

Even if pre-earthquake images are not available, building inventory data might pro-vide useful information on the original height of the building. Building inventory data,especially with building footprint or floor plans, could also help the interpreter under-stand the original state of the building therefore would be useful to understand what hasand has not changed in the post-earthquake imagery.

Shadows are another factor. If there is a damaged object in a shadow, it will not bereadily visible in the satellite image (Figure 9). In such cases, building inventory data ormaps with footprints of buildings would help identify buildings hidden in the shadows.Shadow length can also provide height information of a building, provided that it fallson a flat surface. Methods to calculate building height from shadows have been pre-sented by Jensen (2000). In addition, the shadows will fall in different directions in dif-ferent images, depending on the look angle of the satellite image.

Small wall cracks in standing structures were not visible (for example, Figure 8) andwill not usually be seen on the commercial images currently available, since they are toosmall and because satellite images are taken from above. Even if the satellite image wastaken from an angle oblique enough to see walls of buildings, it is unlikely that smallcracks on the wall would be visible at the present spatial resolution of IKONOS images.

For the same reason, damage to low-rise buildings with small plan area was mostlyunidentifiable (Figure 10). Even the 1-m resolution of IKONOS images is not goodenough to resolve the details of these low-rise buildings. Damage to individual low-rise

Figure 9. Photo (a) of the rubble of a collapsed building. In the satellite image (b), the shadowof the building in the middle of the white circle is ‘‘hiding’’ the rubble. The white arrow showsthe look angle of the photo. The scale of the satellite image is approximately 1:1,500. (Groundphoto by M. Markus.)


buildings located in densely built-up areas is difficult to identify with confidence evenwhen they have totally collapsed. It is easier to identify damage to high-rise buildings(three stories or more).

EXPERIMENT 2: AREA-BASED RAPID DAMAGE ASSESSMENT

METHODOLOGY

The second experiment was designed to test whether area-based visual damage in-terpretation using very high resolution satellite images could produce a damage maprepresenting the true damage state of an area. Since a substantial number of buildingshad collapsed or were severely damaged in Bhuj, the task of identifying every one ofthese buildings on the satellite image would be time consuming, and it is in any casedifficult to identify each separate collapsed building with confidence. Instead, an area-based damage assessment was carried out using the post-earthquake satellite image. Itwas subsequently possible to compare the results of this aerial assessment with the re-sults of a ground survey done by Environmental Planning Collaborative (EPC), a non-profit organization based in Ahmedabad, India, that was commissioned by the local gov-ernment to map the damage in Bhuj.

The experiment was carried out in three steps. First, a grid-based damage assessmentof Bhuj was carried out using visual interpretation. The area outlined in Figure 11 rep-resents the study area. A 100-m-square (Hectare) grid was overlaid on the satellite im-age. Each grid square was assigned a damage level based on predefined criteria as listedin Table 4. These criteria were chosen in order to define a small number of damage lev-els, more suitable for use in visual interpretation than the more qualitative grades (few,many and most) adopted in EMS scale (EMS 98) definition.

Figure 10. Photo (a) of low-rise building with damage, and satellite image (b) showing thesame building circled with white. None of the damage is clear in the satellite image. Whitearrow shows the look angle of the photo. The scale of the satellite image is approximately1:1,500.


The level of damage of an individual grid square was calculated using the followingformula:

C5~A/B!3100 (1)

where A is the number of buildings that was interpreted as collapsed or partially col-lapsed within a single grid, B is the total number of buildings within the grid, and C istherefore the percentage of collapsed or partially collapsed buildings, i.e., damage grade4 or 5 in EMS scale, among the total number of buildings.

The original number of buildings in Bhuj before the earthquake is not known. Inaddition, it was not possible to count the number of collapsed or damaged buildings ac-curately on the satellite image without any prior knowledge of the original buildings.Hence for this experiment both the total number and the number of collapsed buildingswere estimated using visual interpretation.

Figure 11. Image showing the boundary of the walled city of Bhuj. Experiment 2 was carriedout using the grid shown in the figure. The dimension of a grid square is 100 m3100 m.

Table 4. Damage level classification method used inExperiment 2

Percentage ofcollapsed buildings(D4 and D5 EMS

scale) within a gridsquare

Assigned damagelevel Fill color

0–25% 1 Light gray25–50% 2 Gray50–75% 3 Dark gray75–100% 4 Black

No buildings/water/outside study area

0 Transparent


Following the completion of this damage map, it was discovered that damage in thisarea had also been plotted by a ground survey. The latter survey, created by EPC (Figure12), is an aggregated version of a building-by-building assessment carried out within thehistoric walled city of Bhuj, also done by EPC. In the building-by-building survey, allthe buildings were inspected on the ground and assigned a damage level, which werethen aggregated at street block level according to the damage levels defined in Table 4.

The EPC damage map, which is based on street blocks, was resampled to a grid-based map using the grid created for the visual interpretation so that the two could becompared. This was done by first registering the block-based EPC damage map to thesatellite image with the grid. Then each grid square was assigned a new damage level byaggregating the different damage levels on the block-based damage map existing withinone grid square, according to the percentage of the area each damage level covers withinthe grid. The following formula was then used to calculate the new damage level:

Y5~0.25a10.5b10.75g1d!/100 (2)

where Y (0,Y,1) is the resampled damage level, and a, b, g, and d are the percentagesof the area each intensity level cover within that grid. Damage levels 1, 2, 3, and 4 havethus been assigned a ‘‘damage index,’’ 0.25, 0.5, 0.75, and 1, respectively, as described inTable 4. Figure 13 shows the resampled EPC damage map.

Figure 12. Damage map of walled city of Bhuj, created by Environmental Planning Collabo-rative (EPC), a nonprofit organization responsible for mapping the damage in four towns ofGujarat, including Bhuj. Black: damage level 4 (75–100% of the buildings have suffered dam-age level D4 or D5 EMS scale), dark gray: level 3 (50–75%), gray: level 2 (25–50%), and lightgray: level 1 (0–25%). White: no data.


RESULTS

The result of the photo interpretation experiment is shown in Figure 14. A single gridsquare was assessed within a minute. All 126 grid squares were assessed by one photointerpreter in approximately 4 hours.

The differences between the result of the experiment and the resampled EPC groundsurvey are shown in Table 5. The mean of the difference (one damage level) shows thatthe interpreter has, in most grid squares, underestimated the level of damage by one

Figure 13. Resampled EPC damage map (original map in Figure 12) overlaid on the satelliteimage. The damage classification scheme and color scheme used in Figure 12 were both usedagain, i.e., light gray: 0–25%, gray: 25–50%, dark gray: 50–75%, and black: 75–100% (seeTable 4).

Figure 14. Result of the area-based photo interpretation (Experiment 2) using post-earthquakeIKONOS image of Bhuj. Light gray represents grid squares interpreted as damage level 1,meaning that 0 –25% of the buildings suffered severe damage or total collapse (D4 or D5); gray,25 –50%; dark gray, 50 –75%; and black, 75 –100% (see Table 4).


damage level. Nevertheless the overall damage pattern resembles that of the resampledEPC damage map. Standard deviation is 1.58. In summary, 19% of the grid squares wereinterpreted correctly, 41% were out by one damage level, 27% were out by two levels,12% were out by three levels, and one grid square underestimated the damage level byone level.

DISCUSSION

The grid squares highlighted in dark gray and gray in Figure 15 show the 15 caseswhere there was a discrepancy of three damage levels, and the 34 cases where the dis-crepancy was two levels. The reason for these relatively big discrepancies may lie in theresampling method of the EPC map, i.e., the EPC map was resampled into smaller gridsquares than the original building block-based damage survey and therefore theresampled damage level does not represent the true damage level of the area it covers.Figure 16 shows the grid squares that were correctly interpreted. These grid squares are

Figure 15. Dark gray and gray squares show the areas that were assigned damage levels out by3 or 2 levels by visual interpretation, compared to the damage level assigned in the resampledEPC damage map.

Table 5. Experiment 2: Discrepancies between dam-age found from remote sensing and damage recordedfrom ground surveys

Discrepancy(damage levels)

Number ofGrid squares

21 10 241 522 343 15

Total 126


mostly concentrated in the south half of Bhuj, where the building stock mostly consistsof modern buildings with a large plan area. If we look again at the distribution of themore seriously misinterpreted grid squares shown in Figure 15, it could generally be saidthat they are in the areas where small low-rise buildings are concentrated, i.e., northwestof Bhuj. The results of Experiment 1 also suggest that damage to small, low-rise build-ings is difficult to identify using IKONOS imagery. In summary, it can be said that grid-based visual interpretation methods can derive the general damage pattern, and the in-terpretation is more accurate in areas with modern middle- to high-rise buildings with asubstantial plan area.

There is still a lot to be achieved in terms of the accuracy of the interpretation. Al-though the general damage pattern can be derived from pure visual interpretation, theinterpreted damage intensity is not as accurate as it could be. Building footprints may beuseful to improve the assessment of the damage levels of each grid square.

An example of useful information to increase the accuracy of the assessment of thedamage level of a grid square would be a map with building footprints of the area. Dur-ing the experiment, the interpreter encountered difficulty in distinguishing the areas pre-viously occupied by buildings from those which were open space, especially in severelydamaged areas where most of the buildings had collapsed. If the footprints were avail-able, they will help in clearly distinguishing between areas of buildings and nonbuild-ings. The footprint map would also help to quantify damage by providing the total num-ber of buildings within a grid square.

The EPC resampled damage map represents the percentage of damaged buildingswhose damage level can be translated as damage grade 4 and 5 using the EMS 98 scale.From the first experiment described in this study, it has become clear that even buildingswith D4 damage are not always visible on the satellite image. If the EPC intensity maphad only mapped buildings with D5 damage, it is possible that the accuracy of the photointerpretation would have increased.

EXPERIMENT 3: DAMAGE ASSESSMENT EXPERIMENTIN WEST AHMEDABAD

METHODOLOGY

The third experiment was carried out using pre- and post-earthquake images of westAhmedabad. The experiment was designed to test whether by comparing pre- and post-earthquake very high resolution images it is possible to identify partially and totally col-lapsed buildings. For Ahmedabad, since less than 60 buildings had collapsed or partiallycollapsed out of tens of thousands in the city as a whole, the focus was set on finding theindividual collapsed buildings among the buildings intact. Two IKONOS images ofAhmedabad, one taken before the earthquake on 9 November 2000 and another on 27January 2001, a day after the earthquake, were used for this experiment. The experimentwas carried out by visually comparing the pre- and post-earthquake images.

To organize the search, a mesh grid was overlaid on both images in standard GISsoftware. In effect, a systematic ‘‘scan’’ of the entire image using human eyes was car-ried out. The grid size selected was 300 m3300 m, which is roughly the right size for


the human eye to inspect at one glance (Figure 17). It was also the size that would fit a19-inch monitor when zoomed in to the scale (approximately 1:2,500) where individualbuildings could be identified on the screen. Approximately 15 hours was spent on theinterpretation of the whole image (121 km2), carried out by one photo interpreter. Thelook angles of the sensors were different (67 degrees and 61 degrees, respectively) onthe two days, affecting the direction of the tilt of the high-rise buildings and the direc-tions of the shadows.

RESULTS

As a result of this experiment, 12 sites were identified as totally or partially collapsedbuildings, with a high probability. In order to verify whether the 12 buildings had actu-ally suffered such damage, all 12 sites were visited during a field trip in April 2002.Although a 1:20,000 scale city map of Ahmedabad was readily available with some ofthe major road names on it, a map of Ahmedabad detailed enough to identify the loca-tion or name of building was not available prior to the visit. Therefore a method thatwould make it possible to reach the actual location of the suspected collapsed buildingsites had to be devised. This was done by the following two methods.

The first was the creation of a map using the IKONOS satellite image. Each site wasmarked on the satellite image using standard GIS software. Major road names were alsomarked on the map. This map enabled the field team to navigate its way to the sites with-out difficulty. The second method employed was to use a handheld GPS. Before leavingfor the field trip, the coordinates of the 12 building sites were registered in a handheldGPS system. The method proved highly accurate, and one building was reached usingonly the directions provided by the GPS without the help of the map.

The experiment produced very good results. Ten out of the 12 buildings previouslyidentified were found to have indeed collapsed in the earthquake (83% success rate).Photographs in figures 18 and 19 show 2 of the 10 sites where the visual interpretationwas correct, i.e., the building had collapsed. Of the two buildings that were misidenti-fied, one was a four-story building with an irregular shape that gave a similar impression

Figure 16. Gray grid squares show where the damage level was interpreted correctly.


to a collapsed building (Figure 20). The other building was a building site where thebuilding had been demolished before the earthquake (Figure 21), but subsequent to thedate of the pre-earthquake image.

DISCUSSION

This experiment was carried out without having any detailed prior knowledge of thedamage in Ahmedabad. The results derived from purely visual photo interpretation haveshown that individual modern collapsed buildings can be identified with high confidenceby visually comparing the pre- and post-earthquake high-resolution satellite image.

The Gujarat State Disaster Management Authority web site (GSDMA 2003) includesa list of all the 57 collapsed buildings and the 25 buildings that were pulled down due todamage beyond repair. An attempt has been made to try and identify the location of allthese buildings on the detailed map with individual plots, obtained during the field visitin April 2002. Unfortunately, although 3 of the 12 buildings identified in this experimentmatched, the rest were not found to be on the list. This is probably due to the vaguedescriptions of the location of these buildings in the list. Another factor is that there aremany buildings and street blocks with the same name. This suggests that post-earthquake damage mapping done by others cannot necessarily be relied on as a reliablesource of ground-truth data for such a building-by-building assessment. It is also worthnoting that more than half of these collapsed buildings are located in the east ofAhmedabad, which is not covered by the satellite image obtained for this experiment.

APPLICATION IN POST-EVENT DISASTER MANAGEMENT

Two types of output can be envisaged. One is the building-by-building type damageassessment of Experiment 3; the other is an area-based study, Experiment 2. Each typecould have its own range of uses. Building-by-building mapping would be useful for im-mediate relief and rescue operations; area-based damage mapping would be useful for

Figure 17. (a) Full view of the Ahmedabad pre-earthquake IKONOS image (1-m panchro-matic) with the grid covering 121 km2 used in Experiment 3. (b) The dimension of a grid squareis 300 m3300 m; the scale of the image is approximately 1:10,000.


planning temporary settlements as well as recovery and reconstruction. Other maps,showing for instance blocked roads, collapsed bridges, dam failures, and areas of groundliquefaction, could be developed by the visual assessment technique.

The time element is an important factor. Though less accurate, the visual interpreta-tion methods employed in these experiments are very much less time-consuming thanconventional field surveys. The Ahmedabad experiment took approximately 15 hours tocomplete by one image interpreter. If the whole of west Ahmedabad, which containssome tens of thousands of buildings, were to be surveyed building-by-building on theground, it would take weeks to complete the survey. However, even visual interpretationdoes take time, if one tries to apply it to a large area such as all towns and villages in anearthquake-affected region. For this type of event a team of photo interpreters would beneeded.

In terms of the skills required, the interpreter who carries out the visual interpreta-tion in this type of damage assessment should have some experience in visual interpre-tation. However, it is possible for human eyes to be trained for the task quickly, providedthe person has fairly good eyesight. Developing a set of guidelines to assist in the visualdamage assessment would be necessary for this purpose. GIS data management skillswould also be needed to prepare the images for on-screen photo interpretation.

It now takes around two weeks after the payment has gone through for standardIKONOS archive satellite images to be delivered. For a new acquisition order the sup-plier is given a window of 90 days to collect the imagery (in case of IKONOS images—http://www.spaceimaging.com). But many satellite image providers, including those thatoffer very high resolution images, are now starting to offer 24-hour delivery, dependingon the orbit of the satellite and the urgency of the request. Considering the urgency ofdamage assessment for rescue purposes, this is the kind of delivery timescale that mustbe aimed for. The international agreement known as Disaster Charter (InternationalCharter 2003) aims to provide satellite images of a natural or man-made disaster to helpmitigate the effects on human lives and property. The use of this system is rather limitedin the sense that the charter can only be activated through ‘‘authorized users.’’ However,

Figure 18. Example of an image of a collapsed building: (a) pre-earthquake image, (b) post-earthquake image, and (c) ground photograph of the same site 15 months after the earthquake.Scale of the satellite images is approximately 1:2,000. (Ground photo by K. Saito.)


it does have a potential to make satellite images of disaster-affected areas more acces-sible and obtainable in a shorter period of time. For example, the flood imagery suppliedto the local authorities in Germany in August 2002 was obtained through this charter. Acomplete list of events in which the charter was activated can be obtained from their website at http://www.disasterscharter.org/main–e.html.

The cost of the satellite imaging is another issue. For instance, 1-m panchromaticIKONOS images cost $29 per square kilometers (archive imagery). The expense of ac-quiring images could therefore be a significant operational cost. For this reason themethod is likely to be more suitable for use only on towns and cities and other high-value facilities under present commercial arrangements. There is clearly a need foragreements allowing postdisaster satellite imagery to be disseminated to image analystsat minimum cost for relief purposes in the aftermath of major disasters.

Figure 19. Sample image of another collapsed building: (a) pre-earthquake image, (b) post-earthquake image, and (c) the same site 15 months after the earthquake. Scale of the satelliteimages is approximately 1:2,000. (Ground photo by K. Saito.)

Figure 20. Building misidentified as collapsed, referred to as building 3 in the text: (a) pre-earthquake satellite image, (b) post-earthquake satellite image, and (c) image taken during fieldsurvey in April 2002. The building had not-collapsed during the earthquake. The arrow showsthe direction from which the photograph was taken. Scale of the satellite images is approxi-mately 1:2,000. (Ground photo by K. Saito.)


CONCLUSIONS

The three experiments show that 1-m panchromatic IKONOS satellite images canprovide the level of information needed to identify the most damaged areas following anearthquake and to distinguish collapsed buildings from non-collapsed buildings. Theyalso show that visual screening alone, supported by standard GIS and image processingsoftware can enable damage identification and mapping to be done very rapidly. Even atthis level of accuracy, the value of such information to emergency management centersand to rescue teams would be considerable, if it could be made available within the firstdays after the event.

But the study has also identified significant limitations in what can be identified, interms of detail of damage and completeness of coverage. A future study into the use ofhigh-resolution satellite images for post-earthquake damage assessment will try to es-tablish whether image analysis software can be used to enhance the ability of the un-aided eye to detect damage related changes between pre- and post-event image in theoverall geometry of a building. In this next phase, studies of damage assessment meth-ods for modern middle- to high-rise buildings will be considered separately from dam-age assessment methods of low-rise buildings.

Meanwhile, procedures and international protocols need to be put in place for theperiodic capture of images of earthquake prone cities and for both pre- and post-earthquake images to be immediately released at an affordable cost to national and localemergency management agencies at times of need.

ACKNOWLEDGMENTS

The authors are grateful for important contributions to this study made by Dr. S. P.Gopal Madabhushi (Department of Engineering, University of Cambridge, U.K.), R.Balachandra (Environment Planning Collaborative, India), Steve Dolphin and ShaileshTrivedi (Babtie, India), Oliver Peterken and Shigeko Tabuchi (Willis Ltd., U.K.), andJohn Henry (Ove Arup and Partners, U.K.). The original IKONOS images are © 2001,

Figure 21. Building misidentified as collapsed, referred to as building 10 in text: (a) pre-earthquake satellite image, (b) post-earthquake satellite image, and (c) image taken during fieldsurvey in April 2002, 15 months after the earthquake. Scale of the satellite images is approxi-mately 1:2,000. (Ground photo by K. Saito.)


Space Imaging Middle East (LLC), all rights reserved. The authors would like to thankEngineering and Physical Science Research Council, U.K., (EPSRC Award numberRG33119) for funding this project.

REFERENCES

Chiroiu, L., Andre, G., Guillande, R., and Bahoken, F., 2002. Earthquake damage assessmentusing high resolution satellite images, Proceedings of the 7th U.S. National Conference onEarthquake Engineering, Boston, MA.

Coburn, A., and Spence, R. J., 2002. Earthquake Protection, John Wiley & Sons, London.Digital Globe, 2003. Web address http://www.digitalglobe.com/products/quickbird.shtml.Eguchi, R. T., Huyck, C. K., Houshmand, B., Tralli, D. M., and Shinozuka, M., 2000. A New

Application for Remotely Sensed Data: Construction of Building Inventories Using Syn-thetic Aperture Radar Technology, presented at the 2nd Multi-Lateral Workshop on Devel-opment of Earthquake and Tsunami Disasters Mitigation Technologies and Their Integrationfor the Asia-Pacific Region, March 1–2, 2000, Kobe, Japan, available from http://www.imagecatinc.com/reportspubs/reportspubs.html/.

Estes, J. E., and Jensen, J. R., 1998. Development of remote sensing digital image processingand raster GIS, Chapter 10 of the History of Geographic Information Systems—Perspectivesfrom the Pioneers, Prentice-Hall, Upper Saddle River, NJ.

European Seismological Commission, 1998. European Macroseismic Scale 1998, EuropeanSeismological Commission Subcommittee on Engineering Seismology Working GroupMacro Seismic Scale, Luxembourg.

Gujarat State Disaster Management Authority (GSDMA), 2003. Web site http://www.gsdma.org.

Hasegawa, H., Yamazaki, F., Matsuoka, M., and Sekimoto, I., 1999. Determination of buildingdamage due to the 1995 Hyogoken-Nanbu earthquake using aerial HDTV images, Proceed-ings of the Second Conference on the Applications of Remote Sensing and GIS for DisasterManagement, George Washington University.

Huyck, C. K., Mansouri, B., Eguchi, R. T., Houshmand, B., Castner, L. L., and Shinozuka, M.,2002. Earthquake damage detection algorithms using optical and ERS-SAR satellite data—Application to the August 17, 1999 Marmara, Turkey earthquake, Proceedings of the 7th Na-tional Conference on Earthquake Engineering, Boston, MA.

ImageSat International, 2003. Web address http://www.imagesatintl.com/aboutus/satellites/satellites.shtml#.

International Charter, 2003. Web address http://www.disastercharter.org/main–e.html.Jain, S. K., Lettis, W., Murty, V. V. R., and Bardet, J.-P., editors, 2002. Bhuj India Earthquake of

January 26, 2001 Reconnaissance Report, Earthquake Spectra, Supplement to Vol 18, pp.1–398.

Jensen, J. R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective,Prentice Hall, Upper Saddle River, NJ.

Jensen, J. R., 2000. Chapter 6 of Photogrammetry, Remote Sensing of the Environment, PrenticeHall, Upper Saddle River, NJ.

Lillesand, T. M., and Keefer, R. W., 1994. Remote Sensing and Image Interpretation, 3rd edi-tion, John Wiley & Sons, NY.

Madabushi, G. (ed.), 2002. The Gujarat Earthquake of 26.1.01; a Field Study by EEFIT, Insti-tute of Structural Engineers, London.


Matsuoka, M., and Yamazaki, F., 2002. Earthquake damage detection using satellite SAR im-agery, Joint Workshop on U.S.-Japan Cooperative Research in Urban Earthquake DisasterMitigation, January.

Mitomi, H., Matsuoka, M., and Yamazaki, F., 2002. Application of automated damage detectionof buildings due to earthquakes by panchromatic television images, Proceedings of the 7thU.S. National Conference on Earthquake Engineering, Boston, MA.

NPA, 2001. Damage interpretation of Bhuj carried out under the Disasters Charter. See http://www.disasterscharter.org/disasters/india–e.html.

Prasad, R., and Sinha, A. K., 2002. IRS-1B based FCC image interpretation for land use andland cover mapping: An expert system approach, from http://www.gisdevelopment.net/application/urban/overview/urbano00.html.

Space Imaging Inc., 2003. Web address http://www.spaceimagingme.com/sime.asp?page=Constellation.

Steinle, E., and Vogtle, T., 2001. Automated extraction and reconstruction of buildings in laser-scanning data for disaster management, in E. Baltsavias et al. (eds.), Automatic Extraction ofMan-Made Objects from Aerial and Space Images (III), Swets & Zeitlinger, Lisse, The Neth-erlands, ISBN 90 5809 252 6.

Yusuf, Y., Matsuoka, M., and Yamazaki, F., 2001. Damage detection from Landsat 7 satelliteimages for the 2001 Gujarat, India earthquake, Proceedings of the 22nd Asian Conference onRemote Sensing, Singapore.

(Received 6 August 2002; accepted 16 May 2003)

using high-resolution satellite images for post-earthquake building damage assessment: a study...

Documents