FLOOD DAMAGE ASSESSMENT INTEGRATING GEOSPATIAL TECHNOLOGIES. A CASE STUDY IN HUE, VIET NAM

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1 Paper 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 1

2 AMFF-7 - Integrated flood risk management in the Mekong River Basin 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 2

3 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 GIS Layers Microwave Optical Topographic Map (1: 50, 000) Road Map Admin Boundary Radarsat 06/11/1999 During flood Radarsat 15/11/1999 After flood SPOT 5 DEM at 20 m resolution Flood Level recording Geo-rectification Frost filtering Low pass filtering Enhanced images Image classification Supervised/ Maximum Likelihood RGB Composite R: During flood image G: After flood image B: During flood image Image classification Unsupervised/ K-mean Re-Classification Flood Area Extent Land Use/ Land Cover Map Flood Depth Map Raster based GIS Analysis for Rapid Damage Assessment Flood Damage Map SS Figure 2. Flow Chart depicting the Methodology adopted in the Study 2 dis tan ces = x C( x)] x [ (1) 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 3

4 AMFF-7 - Integrated flood risk management in the Mekong River Basin 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. 4

5 Land Use Map Figure 4. Flooding area extent map of Hue City (1999) from classification 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 5

6 AMFF-7 - Integrated flood risk management in the Mekong River Basin 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. 6

7 REFERENCES Dutra, L.V., Sant Anna, S. J.S., The effect of speckle filtering on SAR texture discrimination, Anais VIII Simposio Brasileiro de Sensoriamento Remoto, Salvador, Brasil, April 1996, INPE, p Haralick., Sternberg., Zhuang., Image Analysis Using Mathematical Morphology, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 4, July, pp Lopes, A., Touzi, R., Nezry, E Adaptive Speckle Filters and Scene Heterogeneity, IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 6, pp Tou, J.T., Gonzalez, R.C., Pattern Recognition Principles, Addison-Wesley Publishing Company, Reading, Massachusetts. 7