ACRS 2005 Tsunami Paper Vf

8/9/2019 ACRS 2005 Tsunami Paper Vf

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Tsunami Disaster Damage Detection and Assessment Using

High Resolution Satellite Data, GIS and GPS Case study in

Sri Lanka

Mehdiyev Magsud, Kyaw Sann Oo and Jagath Rajapaksha

Geoinformatics Center, School of Advanced Technologies, Asian Institute of Technology, PO Box 4, Klong Luang,

Pathumthani, Thailand. [email protected], [email protected]

Lal SamarakoonEarth Observation Research Center, Japan Aerospace Exploration Agency, Triton Square Office Tower-X 23F, 1-8-10

Harumi, Chuo-ku, Tokyo, Japan. [email protected]

ABSTRACT: The earthquake triggered near Banda Ache of Indonesia on December 26, 2004 was the fifth most strong

for the last 100 years and the worst in 40 years, registering a magnitude of 9.0. The epicenter was located about 300 km

west of Medan, west coast of the Indonesian island of Sumatra. The earthquake was followed by tsunami, surge of waves

that killed nearly a quarter of a million people, mostly in Indonesia, Sri Lanka, and India. The coastal regions of India,Sri Lanka, Thailand, Indonesia, Maldives, Malaysia, and Myanmar were all severely affected.

This paper summarizes the effort of international agencies taken to utilize satellite remote sensing and other mapping

tools to provide critical information that may have used for various relief activities and subsequent recovery activities.

Services, products provided by various national and international agencies are discussed and summarized. Further, a

summary of field survey conducted in Sri Lanka to identify damages to natural features and manmade features are

presented. Comparison was carried out to evaluate the use of high-resolution satellite data in damage assessment

specifically in severity of building damage, which is a rather complicated using space observation. Assessment to

changes and damage is easily observable in coastal areas, vegetation and water cause and completely damaged houses

and building. There was a difficulty in identifying the gravity of damage to buildings that were not totally collapsed.

Keywords: Remote Sensing, GIS, GPS, High Resolution Satellite Data, Tsunami, Coastal

1. INTRODUCTION

The earthquake triggered near Banda Ache of Indonesia on December 26, 2004 was the fifth most strong for the last 100

years and the worst in 40 years, registering a magnitude of 9.0. The epicenter was located about 300 km west of Medan,

west coast of the Indonesian island of Sumatra. The earthquake was followed by tsunami, surge of waves that killed

nearly a quarter of a million people, mostly in Indonesia, Sri Lanka, and India. The coastal regions of India, Sri Lanka,

Thailand, Indonesia, Maldives, Malaysia, and Myanmar were all severely affected. Bangladesh, the Seychelles, Somalia,

Kenya, and Tanzania also suffered some damage and loss of life but lesser extent. It was found that in certain areas the

wave has risen for more than 10 meters traveling more than 500 km/h taking away anything that come across its path.

Following the UNISPACE III conference held in Vienna, Austria in July 1999, the European and French space

agencies (ESA and CNES) initiated the International Charter "Space and Major Disasters", with the Canadian Space

Agency (CSA) signing the Charter on October 20, 2000. In September of 2001, the National Oceanic and Atmospheric

Administration (NOAA) and the Indian Space Research Organization (ISRO) also became members of the Charter. TheArgentine Space Agency (CONAE) joined in July 2003. The Japan Aerospace Exploration Agency (JAXA) became a

member in February 2005.

The International Charter aims at providing a unified system of space data acquisition and delivery to those affected

by natural or man-made disasters through authorized users. Each member agency has committed resources to support the

provisions of the Charter and thus is helping to mitigate the effects of disasters on human life and property

(http://www.disasterscharter.org/). The only bodies authorized to request the services of the Charter are the authorized

users. An authorized user is a civil protection, rescue, defense or security body from the country of a Charter member.

Those eligible to become members of the Charter include space agencies and national or international space system

operators. Once a request is sent to activate the Charter a sequence of events will take place and requests will be sent to
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Charter members for satellite data. Once the data are received at a coordinating office that is establish for a particular

event receive all data, process if necessary and send to the authorized user.

Once satellite data are received by the coordinating office, the obligation to the Carter activity ends and the

subsequent process and procedures to use the data are depend on the authorized users. Unfortunately, support to use the

data in a particular incident is not fully existence but there are few initiatives by international and NGOs to help a

country in need to provide assistance to use these data. Some of the main user level needs that is required to address are

data sharing issues, data receiving capacities, data receiving infrastructure, analysis capabilities, availability of physical

and social data in digital form, awareness of the capability of GIS and remote sensing.

During the 2004 Tsunami disaster, the United States Government launched post-tsunami relief effort as a joint

project under the terms of the individual ClearView license arrangements with DigitalGlobe, Inc., Space Imaging, LLC,

and ORBIMAGE, Inc. The purpose of this joint project is to provide geospatial intelligence support to any government,

international, or non-governmental organization participating in Indian Ocean disaster response, relief and recovery

efforts. Geoinformatics Center of Asian Institute of Technology (AIT) was recognized as an authorized user for this data

through the kind assistance of United Nation Office of Outer Space (UNOOSA) for helping in mapping damaged areas in

Sri Lanka. This work was conducted jointly with various international and national agencies of Sri Lanka using satellite

data made available under this initiative. This paper specifically targets the rapid mapping of tsunami-disaster affected

areas by integration remote sensing, GIS and GPS technologies, in Sri Lankan city of Galle.

2. Objectives of the Study

The main objectives of this study are the followings:

- Identify damaged areas using pre and post tsunami high resolution satellite imagery

- Identify the interpretation accuracy of satellite derived information compared with ground truth information, GIS

and GPS data with information derived from satellite imagery

- Creation of damage map using visual observation of satellite data.

- Evaluate potential of using of use of high-resolution satellite imagery for post-disaster damage assessment

3. STUDY AREAThe selected study area is City of Galle (highlighted in Figure 1), which is located in the southwest coast of the

country, approximately from 60 117 N to 60 229 N and from 800 1241 E to 800 14 56 E. This city is the largest

urban area in southern Sri Lanka and incurred the maximum economical cost due to Tsunami. This is a port city with a

harbor nestled with commercial, residential and fisheries land usage. The main link between Colombo and southern

cities in the region are national highways and railway line lay few meters away from the coastline. This is a very

significant feature in Sri Lanka, and the main reason for causing more than 30,000 deaths during Tsunami in December

2004 was this population and infrastructure distribution characteristic. Figure 1 and figure 2 shows the extent of damage

to the country.

Galle

Fig.2 Map: Number of Houses Damaged due to Tsunami in

December 2004: Source: NDMC Sri LankaFigure 1 Number of Death due to Tsunami in

December 2004: Source: NDMC, Sri Lanka


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COPYRIGHT 2005 DigitalGlobe, ClearView License

Figure 3 Shows very high resolution QuickBird image of the study area

Figure 3 is a satellite picture of Galle city taken in March 2001. This was generating by fusing panchromatic and multi-

spectral images having spatial resolution 0.67m and 2.4m, respectively. The circular feature in the left of the image is the

International Cricket ground in Galle. This is located next to the central bus stop of Galle. Center o the images, the

Galle harbor is clearly visible with its breakwater and other facilities. National highway is running along the coastal belt

where most of the urban population is concentrated. This is bay b y nature and this topographical characteristic

contributed to concentrate tsunami wave force to raise the wave height to more than 10 meters (according to prints in

building) causing heavy casualties and property damage.

4. METHODOLOGY

Flowchart of methodology is shown in the Figure 4. Data used, procedures, damage detection and map generation

process are described in following section in detail.

Remote Sensing and GIS Data

Preprocessing Procedure

Damage Detection

Combining Analyzed Remote Sensing and GIS

Data

Damage Map Generation

Figure 4 Flowchart of Methodology


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4.1. Data Used in the Study and Data Providers

High-resolution satellite data as well as accurate GIS datasets was used for conducting this study. The description of

datasets, data creation and providers in case of received data are given bellow:

- Ikonos pan sharpened pre-event image of Galle city, for March of 2001, with spatial resolution of 1m. This data

was provided by Urban Development Authority (UDA) of Sri Lanka- QuickBird multi-spectral (res. 2.4m) and panchromatic (res.0.67m) post-event images of 26 January of the year of

2005. Images was acquired with the collaboration of UNOOSA

- Pre-event buildings and contour GIS layers with scale of 1:5000. Dataset was provided by Survey Department of

Sri Lanka

- Post-event building damage data, high accuracy elevation data and some additional information about building s

characteristics. Was collected during field survey on 14-28 February with collaboration of various agencies with

the initiatives of UNU, Tokyo

- Financial support for field work was provided by Digital Asia project of Keio University Japan

4.2. Data Preparation and Preprocessing Steps

All datasets that were described in the section above belongs to different formats, projections, accuracy levels and

some other distortions that usually inherent in data collected in the field. In order to compare them, it is necessary tobring them to same reference system.

1) Preparation of Image Data

For change detection and damage estimation, pre and post tsunami high-resolution images were used for study.

Datasets were received from different sources at different processing levels, different datum, and projections. Ikonos pan-

sharpened image was in Sri Lankan Kandawala TM projection, QuickBird images were in WGS84 datum with

geographic projection. It was decided to convert the datasets to local Sri Lankan projection, as this facilitates comparison

with all existing GIS and other map information.

At first QuickBird images were converted to Sri Lankan local coordinates using conversion parameters provided by

Survey Department of Sri Lanka, but comparison of the two images showed some shift in pixel locations. In order to

solve this problem two images were co-registered by selecting number of corresponding ground control points in both

datasets. ENVI image processing software was used for procedure explained above.

2) Perform Pan Sharpening Technique for QuickBird Image

In order to increase quality and spatial resolution of multi-spectral satellite imagery fusion was carried out on to

QuickBird dataset. Both panchromatic and multi-spectral images have been ortho-rectified, in order to reduce geometry

errors inherent with topography and imagery. The factors contributing to geometric errors include:

- camera and sensor orientation

- systematic error associated with the camera or sensor

- topographic relief displacement

- earth curvature

The RPC (Rational Polynomial Coefficients or Rapid Positioning Coordinates) sensor model were used to ortho-

rectify QuickBird data. The ortho-rectification process combines several sets of input data to place each pixel in the

correct ground location. The offset between mean sea level and the gravitational potential surface known as the geoid is

required so the elevation can be correctly interpreted. Finally, if the source image does not have approximate geo-

location information available, the rough location of the image on the earth's surface must be computed to provide a

location base needed for the RPC transformation. The resulting ortho-image is accurate to real world coordinates. And

the QuickBird dataset itself provided this RPC information.

After both images were ortho-rectified, data fusion was attempted using various pan-sharpening techniques. It was

found that color-normalized Brovey algorithm provide better fused result with the two images available for this study.

Results are shown in Figure 5, Figure 6, and Figure 7. All processing was done by using ENVI image processing

software.


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Figure 6 QuickB i rd Panchroamt ic Image,

Spat ial Resolut ion 0.67 met ers

Figure 5 Quick Bird Mult i -spectral Im age, True Color

Composite, Spat ial Resolut ion 2.4 meters

Figure 7 Quick Bird Pansharpened Image,

Spat ial Resolut ion 0.67 met ers

3) Preparation of Existing Pre-event GIS data

The building layer was acquired from the Survey Department was in the scale of 1:5000 and at local Sri Lankan

projection, but after overlaying with satellite datasets there still was some shift in positioning. In order to fix this

distortion, building vector layer and satellite data were co-registered. Reasons for the shift are not know but these could

have introduced by various transformation that were introduced during various processing levels of satellite dataset.

Provider of satellite data has transformed the image in to WGS84 and re-transformation it to Sri Lankan coordinate


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system could introduce distortions, as there are not linear transformations. Also they are not reversible mapping

functions.

4) Preparation of Post-event GIS Data Collected in the Field

In order to collect ground truth information of damaged areas, high precision (sub-meter accuracy) kinematic GPS

survey was conducted in Galle city. During damaged building survey, positions of four corners of damaged buildings

were collected. While comparing these positions with existed building layer it was found that are they are not exactly

matching. The reason for this slight mismatch was due to the difficulty of locating corners of building that were

damaged during Tsunami. Erroneous points were corrected and matched with existed building vector layer. Photos of

damaged building were synchronized with GPS reading as well. Process explained above shown on followings figures.

Figure 8 Field surveyed area is depicted in Red color

Figure 9 Raw Kinematic GPS data Figure 10 Corrected Kinematic GPS data


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4.3. Damage Detection

1) Comparison of ground GPS photos of the damage buildings and the post-event satellite data.

Here ground photographs of damaged buildings in Galle and the satellite image were compared to evaluate the

potential of using high-resolution satellite data, in this study QuickBird satellite data in identifying building damage,

specifically level of damage. During comprehensive ground survey conducted during February 2005, buildings wereidentified with the level of damaged such as completely destroyed, partially damaged mainly inside, partially collapsed

with roof intact, and slightly damaged. During the fieldwork in Galle, 81 buildings were surveyed and recorded. The

photos of each building were synchronized with high precision kinematic GPS for position. After locating the position of

satellite images, comparison between the damage level in the ground photographs and high-resolution satellite data was

made. Some of the pictures below highlight these comparisons.

Totally collapsed buildings (photos on Figure 12, center of yellow rectangle Figure 11) as well as partially collapsed but

with roof replaced ones (photos on Figure13, right and left sides of yellow rectangle Figure 11) can be identified on

satellite images (Figure 11, yellow rectangle).


Geoinfo, Coastline, Galle City, 16 Feb 2005 Geoinfo, Coastline, Galle City, 16 Feb 2005

Geoinfo, Coastline, Galle City, 16 Feb 2005 Geoinfo, Coastline, Galle City, 16 Feb 2005

Figure 11 QucikBird Post-event Pan Sharpened Image With Overlaid Building Layer of the Damaged Area

Figure 12 Photos of Totally Damaged Buildings

Figure 13 Photos of Partially Damaged Buildings


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The above results suggest that there are several types of building damage that could be easily identified with satellite data

without any additional ground information. But in certain cases, specifically where buildings are partially damaged with

roof intact, it is difficult or not possible to estimate the severity of damage to buildings. In these cases help of additional

information, such as pre-event high-resolution imagery, building height data, building inventory data, footprint data of

buildings, and ground photos could serve as supporting information.

2) Comparison of post-event satellite image with ground truth GIS data

Here we try to identify damage areas by simply visual observation of post-event QuickBird data. After this experiment

we can conclude that heavy damaged area can be easily identified directly from high-resolution satellite imagery

(Figures 14-17). The shape of totally collapsed buildings is irregular and shows up as bright speckles. Results were

verified by overlaying existed pre-disaster building layer and ground truth field data collected after disaster.


Figure 14 QucikBird Post-event Panchromatic Image

With Overlaid Building Layer of the Damaged Area

Figure 15 QucikBird Post-event Pan Sharpened Image



COPYRIGHT 2005 DigitalGlobe, ClearView LicenseCOPYRIGHT 2005 DigitalGlobe, ClearView License

Figure 16 QucikBird Post-event Panchromatic Images





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3) Comparison of Pre&Post event satellite images to identify damage

This exercise was carried out using pre and post-tsunami images of Galle city area. The goal of experiment was to test

the possibility to identify partially and totally damaged buildings by comparing pre and post-event high-resolution

satellite data. The visual comparative analysis was applied. Single buildings that had totally collapsed were clearly

visible and easily identifiable.

Images shown on Figure 18 and 19 were taken over Galle city bus station area that was heavily damaged by tsunami

event. Most of the buildings were totally damaged, and easily seen on satellite imagery. The overlaid building layer

verifies our observations. Some results are given in Figure 20 and 21.


Figure 18 Ikonos Pre-event Pan Sharpened Image with

Overlaid Building Layer of the Damaged Area


with Overlaid Building Layer of the Damaged Area


Figure 20 Ikonos Pre-event Pan Sharpened Image with

Overlaid Building Layer of the Damaged Area


with Overlaid Building Layer of the Damaged Area


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On the other hand even some buildings were heavily damaged (verified in the field), but if roofs were not affected it is

difficult sometime impossible to identify it by observing satellite data. Few examples are given in Figure 22.

A B

C Geoinfo, Damaged Building, Galle City, 16 Feb 2005


Figure 22 Buildings are damaged to unusable state but roof was intact

As can be observed in the figure above, building can be seen on both pre (A) and post-tsunami (B) satellite images as

no damaged, but ground photo shows that building was heavily damaged but roof was not replaced.

Another limitation in identifying damage directly using satellite imagery could be shadows. If the damaged object lies

in the shadow, it will not be visible in satellite image.


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4.4. Mapping and GIS Integration

The methods and results discussed previous section were used to create a damage map of Galle city. This includes

derives information from satellite data, field investigations and other data collected from various sources.

1) Damage Map Generation

By combination of high-resolution satellite imagery, visual interpretation and ground truth GIS data tsunami damage

map of Galle city was generated. The places highlighted in red color on Figure 24 had significant damage caused by

tsunami disaster occurred on 26 December of year 2004


Fi ure 23 Tsunami Dama e Ma of Galle cit

Figure 24 3-D view of Galle City.


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2) 3-D Surface Generation

To further facilitate easy visualization of the areas as well as the damage location for further analysis satellite image

was draped over a digital elevation model (DEM) of the area. Survey department produced 1:5000 topographical maps

supported by surveyed points were used in generating the DEM. The result is shown in Figure 24.

5. CONCLUSION

High-resolution satellite imagery offers new possibilities for the rapid post disaster damage assessment and mapping.

The conducted study shows that high-resolution satellite images can provide the level of information that needed to

identify most damaged areas after disaster happened and to distinguish totally and partially collapsed buildings from not

collapsed. It also shows that visual observation with support of standard GIS and image processing can enable damage

identification and mapping to be done very rapidly. It was observed that difficulty exists in identifying building where

no damaged is caused to their roofs. However detailed ground truth data is required in order to increase accuracy of

results. And also highly recommended that pre and post-event satellite images should be acquire by using same platform

and sensor, with same parameters.

Another observation is on satellite data handling. There are various efforts to provide satellite data during a

disaster including the event considered here. It was found it is not easy to incorporate satellite data to have their full

potential due to poor knowledge of satellite data handling, various projection methods without proper technical

background. Therefore, it is warranted tom provide technical assistance to fully utilize the capability of high-resolutiondata in disaster related applications.

Depending on the application to be carried out and the number of persons involved in the study, a near real time

damage assessment could be possible. The feasibility of the application is conditioned by a fast imagery purchasing,

which must be done immediately after the catastrophe and focused on the major urban zones; in the same time, the cloud

coverage represent obviously an important factor constraining the acquisition. The information can be integrated into GIS

base and transfer via satellite or Internet to the rescue teams deployed on the affected zones. The results of a fast damage

assessment received by field operators could help the civil protection, in order to better coordinate the emergency

operations.

ACKNOWLEDGEMENT

Geoinformatics Center of AIT greatly appreciate and acknowledge the support provided by Brenda Jones, Disaster

Response Coordinator, SAIC, USGS EROS Data Center and Sharafat Gadimova, Office for Outer Space Affairs inacquiring high resolution data.

Further, Center acknowledges all the agencies listed bellow and the participated staff for their voluntary work for

collection of field data during the survey:

The University of Tokyo, Tsukuba University, United Nations University, Environment and Sustainable Development

and United Nations University, Institute for Environment and Human Security. Also, there were number of local

agencies including Central Engineering Consultancy Bureau, Sri Lanka Central Environment Authority, Department of

Coast Conservation, Survey Department, Geological Survey and Mapping Bureau, Irrigation Department, Meteorological

Department, National Aquatic Research Agency, National Building Research Organization, State Development and

Construction Corporation, University of Moratuwa, University of Peradeniya, and University of Ruhuna provided their

kind assistance to complete the intended workload.

Finally, the financial assistance provided to carryout field survey and subsequent analysis Digital Asia project of Keio

University is greatly acknowledged.

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ACRS 2005 Tsunami Paper Vf