ESTIMATING LAND VALUE AND DISASTER RISK IN URBAN AREA IN YANGON, MYANMAR USING STEREO HIGH-RESOLUTION IMAGES AND MULTI-TEMPORAL LANDSAT IMAGES

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1 ESTIMATING LAND VALUE AND DISASTER RISK IN URBAN AREA IN YANGON, MYANMAR USING STEREO HIGH-RESOLUTION IMAGES AND MULTI-TEMPORAL LANDSAT IMAGES Tanakorn Sritarapipat 1 and Wataru Takeuchi 1 1 Institute of Industrial Science, The University of Tokyo, Komaba, Meguro-ku, Tokyo, , Japan, tanakorn@iis.u-tokyo.ac.jp, wataru@iis.u-tokyo.ac.jp KEY WORDS: Land value, Flood risk, Earthquake risk, Metropolitan City ABSTRACT: Disaster is the fundamental problem that causes loss of life and property around the world. Especially, when occurring in urban area which has characters of the high population density, it make negative impact to tremendous people and their property. In order to prepare and mitigate the effect of disaster, Evaluating land value and disaster risk in term of spatial information is necessary. This research proposed a methodology to estimate land value and disaster risk in urban area using remotely sensed data. High-resolution satellite images acquired by GeoEye-1 satellite and past to present medium-resolution satellite images provided by Landsat 1-7 satellites have been used. The high-resolution images have been employed to provide land cover and digital elevation model (DEM) and medium-resolution time series were extracted to get land cover changes. Using both land cover and DEM, height of building that can indicate residential or commercial areas were able to obtain. Land value has been estimated in term of index based on height of building and land cover change. While disaster risk has been assessed by using land elevation and floodway for flood risk, and by using distance from fault line, land slope and indirect age of building for earthquake risk. In our experiment, Yangon, former capital of Myanmar, was concentrated to evaluate since it is the country's largest city with over five million people and has had suffer with flood and earthquake disasters. There are three resultant maps with land value, flood risk and earthquake risk. For validation, the land value map was compared with landmark buildings by surveying. DEM was also compared with land elevation by surveying. 1. INTRODUCTION Under the circumstances such as population growth and economics expanding as well as climate changing, Human beings have always faced many disaster events that cause loss of life and property. Extraordinary, when disaster occurring in urban area which has characters of the highest population density and various human-built features compared to its surrounding areas, the damage of the disaster impacts directly to enormous people and their asset, and indirectly to some connected activities that possibly breakdown the country. In order to prepare and mitigate the effect of disaster, Evaluating land value and disaster risk in term of spatial information is essentially required. For land value, land price estimation has been widely done. The empirical model of land price in urban residential development by using GIS technology was proposed (Xu, 2014). They estimate land price by relating to GIS information. Modeling land price distribution was developed by Hu (Hu, 2013). They used multifractal IDW interpolation and fractal filtering method to asses land price. For assessment of disaster risk, Foudi (Foudi, 2015) introduced spatial flood risk assessment; in case of Zaragoza. They used hydraulic and hydrological information and GIS technology to estimate flood risk. Combining hazard, exposure and social vulnerability to provide lessons for flood risk management was introduced by Koks (Koks, 2015). For estimate of earthquake risk, assessment for earthquake scenario in Tabriz, Iran was introduced (Karimzadeh, 2014). They used GIS information including soil s type and ground water. Assessment of earthquake-induced landslides was proposed by Song (Song, 2012). They focused on Beichuan, China and used Bayesian network with GIS data to estimate earthquake risk area. This research presents a methodology to assess land value and disaster risk in spatial term. Satellite dataset from GeoEye and Landsat 1-7 from 1970 to 2010 were used to estimate land value and disaster risk in Yangon, Myanmar (Morley, 2013). 2. MATERIAL AND METHODOLOGY The satellite images of GeoEye and Landsat 1-7 from 1970 to 2010 were used for this research. The pair of stereo images of GeoEye were extracted to provide Digital Surface Model (DSM), Digital Terrain Model (DTM) and Digital Building Model (DBM). The multispectral images of Landsat in almost each ten years was classified as land cover areas. Using land cover and DBM, height of building that can indicate residential or commercial areas were able to obtain. DTM was classified as level of land elevation, was used to provide floodway and was employed to obtain land slope. The indexes of land value, flood risk, and earthquake risk were manually defined by level of impact factors.

2 Based on these factors, the indexes of land value, flood risk, and earthquake risk were formulated. Finally, land value, flood risk, and earthquake risk maps were generated. The flowchart of our methodology to estimate land value, flood risk and earthquake risk maps was shown in figure The Factors of Land Value and Disaster Risk Figure 1. The flowchart of our methodology For land value, the factor of land value is related to height of building and land cover change. Firstly, based on height of building, high building, which is commercial or industrial buildings, is a high value. While low building, which is a residential building, is a low value. Secondly, based land cover, urban is the highest value area since there are a number of population and human activities. Secondly, Plantation is a source of producing foods. Forest (cleaning air, providing oxygen), lake (human recreation), river (supporting ecosystems) has decreasing land values, respectively. Based on land cover change, if the area that was urban area in the past is a higher value than the area that was other areas such plantation or forest etc. in the past. For flood risk, the factor of flood risk is related to land elevation and floodway. Firstly, for land elevation, the lower land elevation is easier to flood while the higher land elevation is safer. Secondly, for floodway, the floodway is high risk since floodway is the channel or area that the water will discharge when it comes. For earthquake risk, the factor of earthquake risk is related to distance from fault line, land slope and age of building. Firstly, for distance from fault line, the area which is near to fault line is high risk to be damaged when earthquake happens while the area which is far from fault line is less dangerous. Secondly, the area which is located in the high slope area is a high risk to happen land slide during earthquake, in the other hand, the area which is located in low slope (flat area) is more secure. Thirdly, age of building, the old building is easier to collapse while new building is lowly risky to fall down. 2.2 Classification For land value, stereo images of GeoEye were used to provide DSM and orthorectified images. Then DSM was applied by using morphological filtering to obtain DTM. DBM was generated by using equation that DBM equals to DSM minus DTM. Orthorectified images with RGB bands was classified by using Mahalanobis distance method (supervised classification) into 2 classes; vegetation and non-vegetation. Using non-vegetation area and DBM, the height of building was able to obtain. We manually separate types of building into 4 class; (1) building with 2-5 meters such as small house (2) building with 5-10 meters such as big house, small office building (3) building with meters such as big office building, small department store, small Factory, small apartment, Small Hotel (4) building with more than 15 meters such as big shopping mall, big factory, big hotel, big Apartment. Then, land cover class was separated into five classes; (1) Urban (2) Plantation (3) Forest (4) Lake (5) River. We applied supervised classification method for high accuracy. Mahalanobis distance method was used with Landsat multispectral images to classify. The land cover change rules were employed to reduce the noises or to smooth the classification result. The first rule is urban expansion rule. The second rule is deforestation rule. The third is cloud effect removing. For flood risk, for land elevation, we applied K-Means method (unsupervised classification) with DTM and level of elevation could be separated into three classes. Then, Hydrological model to estimate water flow was used with DTM by using hydrology software. As a result, accumulation of water flow was available in this process. Manual

3 thresholding method with multiple threshold was used to separate floodway class into five classes. For Earthquake risk, firstly, slope of land was calculated with length of 100 meters. Then, the land slope was applied by using K-Means method to provide four classes from low to high slopes. Then, for age of building, it is very hard to directly observe age of building. So, we used age of urban area to indicate age of building. We used the classification result from previous step but we merge five classes in two classes; urban and non-urban areas. Then we used urban area in the past with product of building area from previous step, indirect age of building was able to obtain. 2.3 Land Value and Disaster Risk Indexes It is very hard to estimate land value and disaster risk in real value. So we estimated them in term of indexes from 0.0 (The lowest) to 1.0 (The highest) instead. Both land value and disaster risk indexes were manually defined. For land value index, there are two factors to indicate land value. The first factor based on height of building, the index of the building height is more than 15 m. is 1.0 (The highest value), the index of the building height is from 10 m. to 15 m. is 0.75, the index of the building height is from 5 m. to 10 m. is 0.5, the index of the building height is less than 5 m. is Then, the second factor based on land cover change. For land cover area, (1) the index of urban is 1.0 (The highest value), (2) the index of plantation is 0.8, (3) the index of forest is 0.6, (4) the index of lake is 0.4, (5) the index of river is 0.2 (The lowest value). The impact of time among land cover change could be defined that the present effect is more impact than the past effect. In this research, there are five-time images. Using land cover change with time impact, the land value index using land cover change based on linear model was calculated as equation 1. Where (i,j) is a pixel at location (i, j). Using two factors with the equal impact; height of building and land cover change, the integral land value index can be formulated as equation 2. (1) For flood risk index, there are two factors to indicate flood risk. For the first factor based on land elevation, the index of class 1 (with average of elevation with 3.4 m.) is 1.0 (The highest risk), the index of class 2 (with average of elevation with 12.7 m.) is 0.67, the index of class 3 (with average of elevation with 23.3 m.) is For the second factor based on floodway, (1) the index of floodway with accumulation of water flow with more than 10,000 is 1.0 (The highest risk), (2) the index of floodway with the accumulation with 5,000-10,000 is 0.8, (3) the index of floodway with the accumulation with 2,500-5,000 is 0.6, (4) the index of floodway with the accumulation with 1,250-2,500 is 0.4, (5) the index of floodway with the accumulation with less than 1,250 is 0.2. Using two factors; land elevation and floodway, the integral flood risk index with equal impact factors can be formulated as equation 3. (2) For earthquake risk index, the first factor based on distance from fault line, the nearest faultline of Yangon City is Sagaing faultline. It was located in eastward of Yangon. The index that is the nearest from fault line is 1.0 (The highest risk) and the index that is the furthest from fault line is 0.0 (The lowest risk). The second factor based on land slope, the index of class 1 (with average of slope with 2.68 degree.) is 1.0 (The highest risk), the index of class 2 (with average of slope with 1.38 degree.) is 0.75, the index of class 3 (with average of slope with 0.57 degree.) is 0.50, the index of class 4 (with average of slope with 0.55 degree and on flat area.) is The third factor based on indirect age of building, for land cover, building area is 1.0 and non-building area is 0.4. The impact of time among building in the past could be defined that the old building is more dangerous than the present building. Using land cover change with time impact, the earthquake risk index using indirect age of building based on linear model was calculated as equation 4. (3)

4 Using three factors; distance from fault line, land slope, age of building, the integral earthquake risk index with equal impact factors can be formulated as equation 5. (4) 2.4 Land Value and Disaster Risk Maps The land value and disaster risk maps were computed using the indexes of land value and disaster risk, respectively. To smooth the disaster risk and land value maps, the mean filter with 5x5 pixels was applied to the map and generated smoothed map for disaster risk and land value. In addition, loss risk map was introduced by integrating between land value and disaster risk maps. Flood has characters of high happening frequency but low damage while earthquake has characters of low happening frequency but high damage. Loss risk map in term of frequency could be formulated as equation 6. (5) Loss risk map in term of damage could be calculated as equation 7. (6) 3. EXPERIMENT AND RESULT The experiment was concentrated on the areas in Yangon, Myanmar. Yangon, former capital of Myanmar, is the country's largest city with over five million people and has had a series of disasters such as flood and earthquake. In this research, there were stereo images in 2013 and five multispectral images from Landsat 1-7 from 1970 to 2010 ant the details was shown in table 1 and samples of the images in this research were displayed in figure 2-5. Table 1. Details of satellite dataset (7) No. Satellite Bands Resolution Acquired Date 1 GeoEye mx0.5m 2013/11/08, 2013/11/16 2 Landsat m. x 60m Landsat m. x 60m Landsat m. x 30m Landsat m. x 30m Landsat m. x 30m

5 Figure 2. GeoEye-1 on 2013/11/08 and 2013/11/16 (Red: Red, Green: Green, Blue: Blue) Figure 3. Landsat -1 on (Red: NIR, Green: Red, Blue: Green) Figure 4. Landsat -4 on 1990/11/12 (Red: Red, Green: Green, Blue: Blue)

6 Figure 5. Landsat -5 on (Red: Red, Green: Green, Blue: Blue) In our experimental result, there are three resultant maps; (1) Land value map, (2) Flood risk map, (3) Earthquake Map (illustrated in figure 6-8). Figure 6. Land value map (Red: High land value, Blue: Low land value) For land value map, the areas could be distinguish into three types; (1) high land value areas that are with high buildings and was urban area in the past (2) medium land value areas that are with medium building or mixed between high and low buildings (3) low land value areas that are low buildings and it look like new urban (they were not urban areas in the past)

7 Figure 7. Flood risk map (Red: High flood risk, Blue: Low flood risk) For flood risk map, there are mainly two types; (1) high risk areas that are located in flat areas and there are a lot of floodways (2), low risk areas that are located in mountain areas but there are still some floodways. Since flood in urban area usually cause from heavy rain in raining season or storm, it depends on drainage system and floodway in that areas. So, even if the area located in high elevation, when heavy rain happening, flash flood can occur in that area if without efficient drainage system. Figure 8. Earthquake risk map (Red: High earthquake risk, Blue: Low earthquake risk) For earthquake risk map, since Yangon city are located in mountain areas, (1) there some areas with high slope. (2) Some buildings are very old especially; in center of urban areas. (3) The areas located in the eastward are more high risks than located in the westward since the nearest faultline is located in the eastward. According to three areas, they are risky to damage when earthquake occurring. In addition, Loss risk maps in term of frequency and damage have been illustrated in figure 9 and 10, respectively.

8 Figure 9. Loss risk map in term of frequency (Red: High land value, Blue: Low land value) Figure 10. Loss risk map in term of damage (Red: High land value, Blue: Low land value) In general, high land value areas (center of urban area or commercial area) should be properly located in safety zone areas which are not risky for flood and earthquake, while low land value areas are located in high disaster risk areas. In fact, some high value areas are still located in high disaster risk area (intensive red color areas in loss risk maps that calculated from land value and disaster risk maps). Loss risk maps can detect the remarked-loss areas in term of frequency and damage. In term of frequency, it can indicate where the areas are high frequency of effect of disaster. In term of damage, it can find where the areas are high colossal loss when disaster occurring. This information can be used for supporting urban planning and management to prepare and mitigate the effect of disaster. To validate our proposed method, for land value map, we compare it with surveying landmark buildings with 492 locations. There are three types of landmark buildings; (1) shopping center buildings with 54 locations, (2) Hotel buildings with 215 locations, (3) Apartment buildings with 223 locations. This validated data was provided by International Center for Urban Safety Engineering (ICUS), The University of Tokyo, Japan. The comparison result show that mean of land value index of shopping center buildings is 0.71, mean of land value index of hotel buildings is 0.66 and mean of land value index of apartment buildings is These areas with shopping center, hotel and apartment have higher land value areas when comparing with residential areas. Since DTM that was generated by stereo GeoEye images were used as information to calculate both flood risk and earthquake risk also DBM for computing height of buildings, DTM was compared with surveying data with elevation level in term of mean sea level with 98 locations (provided by ICUS). The root mean square error (RMSE) between the DTM and validated data is 1.60 m. While the RMSE between SRTM DEM (Shuttle Radar Topography Mission) and validated data is 6.20 m.

9 4. CONCLUSION This research proposed a methodology to estimate land value and disaster risk areas in Yangon, Myanmar using remote sensing technology. The satellite images of GeoEye-1 and Landsat 1-7 from 1970 to 2010 were used in this research. Based on factors of land value and flood and earthquake risks, the indexes of land value and flood and earthquake risks were formulated. Then, the land value and flood risk and earthquake risk maps were generated using these indexes. In our experimental result, there are three resultant maps; (1) land value map, (2) flood risk map, (3) earthquake risk map. In addition, the loss risk maps in term of frequency and damage were introduce to detect remarked-loss areas. For validation, land value map was compared with surveying landmark buildings. Also DTM that was used to estimate flood and earthquake risks was compare with surveying land elevation. These resultant maps can be used as support information for urban planning and management to prepare and mitigate the result of disaster. Acknowledgement This research was supported by Japan Science and Technology Agency (JST)/ Japan International Cooperation Agency (JICA), Science and Technology Research Partnership for Sustainable Development Program (SATREPS). References Foudi, S., Osés-Eraso, N., Tamayo, I., Integrated spatial flood risk assessment: The case of Zaragoza. Land Use Policy, 42, pp Hu, S., Cheng, Q., Wang, L., Xub, D., Modeling land price distribution using multifractal IDW interpolation and fractal filtering method. Landscape and Urban Planning, 110, pp Karimzadeh, S., Miyajima, M., Hassanzadeh, R., Amiraslanzadeh, R., Kamel, B., A GIS based seismic hazard,building vulnerability and humanloss assessment for the earthquake scenario in Tabriz. Soil Dynamics and Earthquake Engineering, 66, pp Koks, E.E., Jongman, B., Husby, T.G., Botzen, W.J.W., Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environmental science & policy, 47, pp Morley, I., Rangoon. Cities, 31, pp Song, Y., Gong, J., Gao, S., Wang, D., Cui, T., Li, Y., Wei, B., Susceptibility assessment of earthquake-induced landslides using Bayesian network: Acasestudy in Beichuan,China. Computers & Geosciences, 42, pp Xu, Z., Li,Q., Integrating the empirical models of benchmark land price and GIS technology for sustainability analysis of urban residential development. Habitat International, 44, pp