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Flood damage assessment integrating geospatial technologies. a case study in Hue, Viet Nam
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Paper 5-4-2
FLOOD DAMAGE ASSESSMENT INTEGRATING GEOSPATIAL
TECHNOLOGIES. A CASE STUDY IN HUE, VIET NAM
DINH NGOC DAT, J. S. M. FOWZE, NGUYEN DUONG ANH, MANZUL K. HAZARIKA
AND LAL SAMARAKOON
GeoInformatics Centre of Asian Institute of Technology (AIT), Pathumthani, Thailand
ABSTRACT
Viet Nam which has more than 3,000 km of coastal line is received with many tropical storms
from East Sea every year. Storms of destructive nature causing considerable damage, generally,
strike the central part of Viet Nam; namely three provinces, HaTinh, Thua Thien Hue and
QuangNam. The 1999 event has been the worst as regards to storms and associated flooding in
Viet Nam causing significant damages. Particularly, in Hue, since 1999, hundreds of people
have lost their lives and the property damage comes amounts to thousands of billions Viet Nam
dong. With climate change scenarios, it is anticipated that similar events of low frequency could
revisit the country with devastating effects. Having identified the effectiveness of the modern
day geospatial technologies such as remote sensing, GIS, and GPS, in hazard, vulnerability and
risk analyses as well as risk management, it is aimed to delineate the flooding extents and map
the elements at risk towards increased awareness, preparedness and sustainable development.
With their proven advantage on the detection of water bodies, RADAR satellite imageries
acquired during the 1999 extreme event and images of the same kind acquired during dry
seasons were made use of to extract the flood area extent by both Red-Green-Blue composition
and image classification after filtering speckle noise and image enhancement. Multi spectral
optical satellite imageries were used for land cover and land use classification by the supervised
classified technique. A field visit was made in this conjunction for observing the ground truth.
Finally, a GIS analysis was carried out for a quantitative analysis and assessment of damage.
Damage curve models corresponding to the identified land use land cover classes were assumed
for the purpose. Attributing total damage costs for the different land cover land use classes the
total damage was arrived at, demonstrating a methodology for assessing the flood damage with
the integration of geospatial technologies and tools.
INTRODUCTION
The flooding event which affected Viet Nam in late 1999 has been recorded as the worst
flooding event the country had experienced in a century. The floods were caused by a series of
storms that brought heavy rain to the central part of the country in October and November. The
first storm to hit was Tropical Storm Eve on October 19 and the main event occurred from
November 1 - 6. In total 793 people lost their lives and 55,000 were made homeless. The floods
brought US$ 290 million of damage to the region and caused a further US$ 490 million of
economic losses. It is estimated that 1.7 million people in the central Provinces of Viet Nam
were affected by the floods.
Important is the fact that Viet Nam gets lashed by typhoons and tropical storms every
year, mostly along the central coast. Further, unprecedented occurrences of rainfall events
linked with climate and which led to flooding have also been reported in the recent past
resulting in loss of life and considerable damage to property. These indicate the need for efforts
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AMFF-7 - Integrated flood risk management in the Mekong River Basin
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in efficient and effective flood risk management strategies towards human security as well as
reduction of damage. A study was thus formulated to estimate the impact of flood hazard in
Thua Thien Hue by using modern day geospatial technologies assisting the local government in
the development of a soft engineering methodology for mitigating flood damages.
STUDY AREA
Thua Thien Hue Province is located at the central part of Viet Nam (Figure 1). It is made up of
four zones; mountain area, hills, plains and lagoons separated from sea and sandbank.
Mountainous area covers more than half the total surface of the province with a height varying
from 500 to 1480 meters. Hills cover one third of the area of the province between the
mountains and the plains with heights varying from 20 to 200 metres. Plains cover about a tenth
of the surface area with height of only up to 20 m+MSL (mean sea level). Lagoons separated
from the sea by sandbanks. These are between the hills which occupy the remaining 5% of the
surface area of the province. Thua Thien Hue has an annual precipitation of around 3,200 mm of
about 70% of which is distributed from September to December.
Figure 1. Location map of the study area
Hue city which is the capital of Thua Thien Hue province falls in the Basin of Huong
River. Eastern of Thua Thien Hue is covered by East Sea and opposite direction it is hugged by
TruongSon Mountains. Being frequently affected by strong tropical storms from the East Sea
and because of its low altitude topography, this city was chosen for the study.
METHODOLOGY
The methodology adopted in the study is shown in Figure 2 in the form of a flowchart.
The RADARSAT satellite data acquired during and after the 1999 extreme flooding event
were processed to extract the flood extent. These images were processed for speckle reduction
by applying the enhanced frost filter (Lopes and Nezry, 1990) while simultaneously preserving
texture information. The enhanced frost filter is an adaptation of the frost filter and similarly
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Flood damage assessment integrating geospatial technologies. a case study in Hue, Viet Nam
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uses local statistics (coefficient of variation) within individual filter windows. The low
convolution filter (Haralick et al, 1987) was then applied to smooth the low frequency
components of the image such as water, wetland, etc. The above filtering techniques have been
checked by Dutra and Sant Anna (1995). Subsequently, Red-Green-Blue composite as well as
classified image were produced for extracting the flood area extent. K-Means unsupervised
classification technique was used for the classification (Tou and Gonzalez, 1974). This
technique calculates initial class means evenly distributed in the data space then iteratively
clusters the pixels into the nearest class using a minimum distance technique. The objective
function is the Sums of Squares Distances - errors (equation 1) between each pixel and its
assigned cluster centre where C(x) is the mean of the cluster that pixel x is assigned to.
Minimizing the SS distances is equivalent to minimizing the Mean Square Error.
Satellite Images
Radarsat
06/11/1999
During flood
GIS Layers
Microwave
Optical
Radarsat
15/11/1999
After flood
SPOT 5
Topographic Map
(1: 50, 000)
DEM at 20 m
resolution
Road Map
Admin
Boundary
Flood Level
recording
Geo-rectification
Frost filtering
Image classification
Low pass filtering
Supervised/ Maximum
Likelihood
Enhanced images
RGB Composite
R: During flood image
G: After flood image
B: During flood image
Image classification
Unsupervised/ K-mean
Flood Area Extent
Re-Classification
Land Use/ Land Cover Map
Flood Depth Map
Raster based GIS Analysis for
Rapid Damage Assessment
Flood Damage Map
Figure 2. Flow Chart depicting the Methodology adopted in the Study
SS dis tan ces = ∑ [ x − C ( x)]2
(1)
∀x
The multi-spectral SPOT image covering the study area was used to prepare the land use
map; the supervised classification technique with the maximum likelihood algorithm was
applied. Necessary ground truth data were incorporated. During the field visits, residents of the
area were also interviewed regarding the water levels during the 1999 flood event. Recordings
on the same made by the meteorological department were also obtained. These data on flood
depth were then utilized together with the digital elevation model created from the topographic
data at a resolution of 20 m by 20 m for preparing the flood depth map of the study area.
Finally, a GIS analysis was carried out developing a methodology in ArcGIS environment
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for rapid flood damage assessment. The raster based model developed for the purpose was
based on the land use information. The produced vector theme was therefore rasterized at a
pixel size of 20 m by 20 m corresponding to the prepared flood map. A duplicate raster data set
was created with an attribution of logical maximum cell damage costs depending on the land
use categories producing the maximum cell damage cost map.
The versatile raster GIS tools were then conveniently utilized to identify the flood depth
and water level at each cell location and thereby to attribute each cell with the associated
damage factor from the damage curves producing the damage factor map. Appropriate damage
models were assumed in the absence of developed damage curves. The intended damage map of
the area in question was produced by raster multiplication of the damage factor map and the
max cell damage cost map. The total damage caused by the event of concern was finally
obtained by summing up the derived cell damage costs.
RESULTS AND DISCUSSION
Flood area extent map from the RGB composite
The flood area extent map prepared by the traditional method of RGB compositing the RADAR
image is shown in Figure 3. Having assigned the red, R, and blue, B, colour guns with the image
during flood and the green, G, colour gun with the image after flood, the flood area extent
appears in the colour shown in the legend. The advantage of this method is the rapid extraction
of the flooding area extent.
Figure 3. Flooding area extent map of Hue City (1999) from RGB Composite
Flood area extent map from image classification
Figure 4 shows the flooding area extent map obtained through image classification. Although
the method is time consuming, is compromised with the quality of the output. Extraction of
flooding area, particularly in the urban areas, is enhanced by this technique. The flooded area
extent as obtained makes the reasons clear for the devastations it had caused inundating almost
the whole of the Hue City.
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Flood damage assessment integrating geospatial technologies. a case study in Hue, Viet Nam
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Figure 4. Flooding area extent map of Hue City (1999) from classification
Land Use Map
Figure 5 shows the land use map of Hue city derived from the SPOT 5 image. Six main land use
classes identified were agriculture, aquaculture, barren land, settlement, shrub and water.
Figure 5. Land use map of Hue City
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Flood damage map of Hue city corresponding to the 1999 flooding event
The flood damage map derived from the GIS analysis is shown in Figure 6. Based on the land
use type and the assumed damage models a majority of the area is identified to be falling under
damage cost range of 2 to 4 million of Vietnam Dong. With the actual damage curves, the
developed methodology could easily be implemented to arrive at the intended damage estimate
rapidly.
Figure 6. Flood damage map of Hue City corresponding to 1999 flood event
CONCLUSIONS
With its proven advantages the RADAR remote sensing technology was successfully utilized to
extract rapidly the flood area extent in the worst flood hit city in Viet Nam. The conventional
RGB composition and image classification techniques were made use of. It is found that the
flood area extraction using RADAR data by creating RGB composite though simple is less
accurate compared to that extracted by the classification technique.
The study has also output a methodology for rapid flood damage assessment the ArcGIS
environment based on the land use information.
ACKNOWLEDGEMENT
The authors wish to thank the Japan Aerospace Exploration Agency (JAXA) for providing
financial support through Mini-Projects, and Space Technology Institute, Viet Nam Academy of
Science and Technology and Remote Sensing Centre, Ministry of Environment and Resources
of Viet Nam for providing with the data used in this study.
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Flood damage assessment integrating geospatial technologies. a case study in Hue, Viet Nam
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REFERENCES
Dutra, L.V., Sant’Anna, S. J.S., 1996. The effect of speckle filtering on SAR texture discrimination, Anais
VIII Simposio Brasileiro de Sensoriamento Remoto, Salvador, Brasil, 14-19 April 1996, INPE,
p.839-843.
Haralick., Sternberg., Zhuang., 1987. Image Analysis Using Mathematical Morphology, IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 4, July, pp. 532550.
Lopes, A., Touzi, R., Nezry, E. 1990. ‘Adaptive Speckle Filters and Scene Heterogeneity,’ IEEE
Transactions on Geoscience and Remote Sensing, Vol. 28, No. 6, pp. 992-1000.
Tou, J.T., Gonzalez, R.C., 1974. Pattern Recognition Principles, Addison-Wesley Publishing Company,
Reading, Massachusetts.
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