URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES

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1 Geoinformatics, vol.21, no.3, pp , URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES Pham Thi Mai Thy*, Venkatesh Raghavan*, N. J. Pawar** * Graduate School for Creative Cities, Osaka City University Sugimoto, Sumiyoshi-ku, Osaka , Japan ptmthy@media.osaka-cu.ac.jp ** Shivaji University, Vidyanagar, Kolhapur , India (Recieved: 2 March 2010; Accepted: 30 June 2010) Abstract : An investigation on urban change detection was undertaken in Can Tho City, Vietnam, situated on the southern bank of the Hau River in the Mekong Delta. By using multi-temporal satellite images from 1972 to 2007, spatial urban expansion in Can Tho City and temporal trends in its urban development were elucidated to comprehend the city's evolution during different periods. The Vegetation-Soil-Water (VSW) index was applied for calculating the built-up areas, while the Normalized Difference Vegetation Index (NDVI) was adopted for enhancing accuracy of land cover classes. Land cover classification based on the Support Vector Machine (SVM) was found to provide better results than the Decision Tree (DT) method that was used by previous researchers such as Huang et al. (2002), and Shafri and Ramle (2009). The study revealed that the main direction of urban expansion in Can Tho City was north-westerly during the last 10 years followed by the rapid enlargement of the city along South-East and South-West directions. The results suggest that the more recently urbanized regions may become economically important, and therefore require quick improvements in social, economic and environmental planning. It was further observed that central districts have a population density 7 to 19 times higher than rural districts. Planned urbanization in the western direction needs to be encouraged in order to provide more opportunities for international trade and tourism in Kien Giang province due to its closer proximity with Thailand, Cambodia and Malaysia. On the other hand, the central areas of Can Tho City witnessing a high rate of urbanization may face risk of flooding and environmental degradation especially during the rainy season. Measures for better flood management in the outskirts of Can Tho City, using high resolution satellite images also need to be evaluated, with some urgency. This study is considered useful for planners and decision makers interested in urbanization patterns of Can Tho City, which is rapidly emerging as a growth center poised to become the economic hub of the Mekong Delta by Key words : Urbanization, Land cover change, VSW Index, Decision Tree, SVM, NDVI. 1 Introduction Use of satellite remote sensing data and application of GIS technologies has quickly emerged as a useful complement to conventional methods, as a means of rapidly assessing the dynamics and development of urban areas so that timely action could be taken by city planners (Farooq and Ahmad, 2008). Studies undertaken recently by the scientists across the globe include Sudhira et al. (2003), Huang et al. (2002) and Wang et al. (2008). Urban expansion is an important topic with many problems associated with growth that can benefit from timely planning and decision making. In Vietnam, since the 1986 launch of the Doi Moi policy toward a market oriented economy, thus confining the state sector primarily to framework economic activities and intervening policies, rapid urbanization posing many challenges (Duong Quang Nghi, 2008). Consequently, urban development and economic growth has flourished, demand in terms of civil infrastructure and industries increased demands on land resources. Rapid industrial growth concurrent with rapid urbanization is evidenced by population growth and expansion of urbanized areas in several cities. At the community/neighborhood levels this has enormously increased pressures limited land resources and resulted in rapid change it land-use patterns. Can Tho City is one such impacted urban area. It is growing at rapid rate, with population of 1.17 million (fourth largest city after Ha Noi, Ho Chi Minh, and Hai Phong as per Government statistic in 2008) and the density of 836 people/h,

2 148 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar which is the highest in Mekong Delta - as compared to 117 thousand in This increase in population has caused problems not only from a social, but also from the environmental point of view (e.g. floods, pollution). For example, of the 230 thousand houses in Can Tho City, nearly 70 to 75% experience drainage problems and about 10 to 15% are built over streams and canals. The heterogeneous urban infrastructure has varying population density ranging from 7,000 people/h in the Ninh Kieu District to only 381 people/h in the Vinh Thanh District (Fig.1). Uneven growth in the city is likely to impact future expansion and hence, to plan future development and urbanization certain studies would be very helpful. Therefore, remote sensing and GIS are used here to provide urban managers with background knowledge to help guide of the planning of urban development in Can Tho City. Figure 1. Distribution of population density of districts within Can Tho City, Vietnam. The main objectives of the article are to determine the present land-use/land cover pattern, to detect the rate of spatial urban expansion, and to establish temporal trends in urban development better to comprehend Can Tho City s evolution over time. For this purpose, multi-temporal satellite images (Landsat MSS, TM, ETM+, JERS-1 and ASTER) from 1972 to 2007 have been used (as available since the launch of Landsat 1 in 1972). In addition, topographical maps of the area were also used for reference, and to understand broadly the nature of land-use before the advent of satellite remote sensing techniques. It is expected that the results will be helpful in re-evaluating governmental strategic development plans for the region that was proposed in 2004 and provide a more up-to-date development scenario in order to build more appropriate future city planning strategies for stable and sustainable development of the region. 2 Study area The present study has been carried out in Can Tho City of Vietnam (Fig.2). It is the second largest and most industrialized city in southern part of Vietnam. The city has an area of 1402 h and is positioned on the south bank of the Hau River and is spread over alluvial plains of Mekong Delta. The topography of the area is relatively flat with elevation ranging from 0.6 to 0.8 m above mean sea level. The city enjoys warm and humid climate with an average temperature around 27 and an average humidity of about 83%. There are two separate seasons namely, the rainy season (May to November) and the dry season (December to April) with an average annual rainfall varying from 1,500 to 1,800 a. Can Tho City is well known for commercial, cultural, scientific and technological activities. It is divided into 8 districts (Vietnam Government, 2007). It connects the provinces of the delta with Ho Chi Minh City (the largest metropolitan area of Vietnam), and neighboring Cambodia. Four districts in Can Tho are categorized as urban (Ninh Kieu, Binh Thuy, Cai Rang and O Mon) based on the population density and main socio-economic activity (non-agricultural) of residents. Based on the same criteria, the remaining four districts (Phong Dien, Co Do, Thot Not and Vinh Thanh) are categorized as rural. Before 1992, Can Tho City was part of Can Tho Province and was recognized as a Class-II City. However, in early 2004, Can Tho Province was split into Hau Giang Provice and Can Tho City, which is centrally governed now (Nguyen Thiem, 2004). As a result of this, during the last few years, the municipal economy grew at an average rate of greater than 15% annually and the city is expected to be a Class-I City 1 in 2010 (Nguyen Thanh Son, 2004) that in turn is likely to transform the city into the Mekong Delta s economic hub by Can Tho City is dissected by many small and larger rivers. They are a convenient and cheap mode of transport of goods from local areas to neighboring provinces as well as to neighboring countries like Cambodia. The main National Highway No. 1 (Fig.2) connects Can Tho City with Ho Chi Minh City 179 km to the northeast. Widening of roads, provision of ferries and bridges across rivers have changed the transport infrastructure and traffic situation of the city. The domestic airport, which was reconstructed from a military airfield, is also currently in operation and will soon be upgraded to international airport by the end of All these factors have arisen from, and contributed to, rapid urbanization of the city. 3 Methodology Several authors have successfully used the Vegetation-Soil- Water (VSW) index with the aid of Decision Tree (DT) classification for long-term monitoring of urban growth in cities such as Tokyo and Bangkok (Yamagata, 1999; Shigenobu et al., 2002). Though, the present study also uses the DT classifier 1 Class-I city is defined in Vietnam Decree 42/2009/N-D-CP as a center of a particular. 2 News from Ministry of Natural Resources and Environment website.

3 URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES 149 Figure 2. Districts within Can Tho City, Vietnam. method, it is essentially used in combination with the Normalized Difference Vegetation Index (NDVI) to distinguish between dry soil and built-up areas. Features classified by using both methods have been used to assess and compare the methods in terms of accuracy. A flowchart of steps followed in this study is presented in Fig.3. The input data are a multi-temporal collection of imagery dataset. The dataset was pre-processed as described below, then the VSW index and NDVI were calculated from the imagery. Later, the classification was carried out by using the DT method and Support Vector Machine (SVM). The built-up areas for each year were then extracted to estimate the rate of urban expansion. and ASTER (February 2007) with a ground field of view ranging from 80m to 15m. Detailed information about the data and the boundary are given in Table 1 and Fig.4. The data from the satellite images for 1972 had low resolution (80m) and was also affected directly by cloud cover over urban areas, which is likely to lead to some discrepancy in the built-up areas detection results. Although the data for 2002 and 2007 were also affected by cloud cover, it was confined more to rural areas. Hence, usability of the data in detecting urban areas is acceptable. The remaining data of 1989 and 1997 had no cloud cover or any other apparent undesirable characteristics. Table 1. Satellite data for Can Tho City, Vietnam Pre-processing At first, five satellite images were converted from Digital Number (DN) into radiance values (Robert, 2007; Milder, 2008; NASA, 2009). Then the ASTER image was chosen as a base map for geo-referencing in the multi-temporal dataset in WGS84 (UTM Zone 48) projection. All the data sources were re-sampled into a 15m resolution. The root mean square error for the geo-referencing process was about 0.6 pixels. Figure 3. Flowchart of satellite data processing Data source The data used in this paper were images acquired by the satellites: Landsat MSS (December 1972), Landsat TM (January 1989), JERS-1 (March 1997), Landsat ETM+ (December 2002) 4 Results VSW index Data used in this paper were different satellite images acquired for different time periods. From these, the VSW index was calculated for extracting the built-up areas to detect the rate of urban expansion over these time period. The VSW index was developed by Yamagata (1999) based on the Perpendicular Vegetation Index (PVI). It is based on scatter plots of pixel values of red and near-infrared (NIR) wavelengths shown on x-axis and y-axis, respectively (Fig.5). The triangle derived from a scatter plot is formed by the vectors that represent water, vegetation and soil. The distances from the spectrum point P to each edge of the triangle PW, PV, PS (value for water, vegetation and soil respectively) can be calculated using the expression developed by Yamagata (1999). This index can detect water, vegetation and soil as it can support three values. In the study, the VSW index was employed to detect built-up areas and discern urban expansion over the available time-series. For this purpose a plot of red and near-infrared reflectance values was made (Fig.5). In Fig.5, to derive the scatter gram and end-members of the

4 150 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar triangle, two (red and near-infrared) wavelength bands were used. The distance of PV, PS and PW were calculated based on the equation: NIR V a V Red V b V PV 1 a 1 2 NIR S a S Red S b W PS 1 a 2 2 NIR W a W Red W b W PW 1 a W 2 where, Red V, NIR V : Red and NIR reflection values intersection point V Red S, NIR S : Red and NIR reflection values intersection point S (a) Red W, NIR W : Red and NIR reflection values intersection point W a V,b V : Parameter of line 1 a S,b S : Parameter of line 2 a W,b W : Parameter of line 3 The parameters of a V, b V, a S, b S, a W, b W are shown in Table 2 for time series. The images were extracted for PV, PS and PW which are shown in Fig.6. The brighter sections of the image in Fig.6(a) show higher reflectance values due to the presence of vegetation while the darker areas represent less vegetation cover. Similarly in Fig.6(b) the darker areas coincide with less or bare soil, while the lighter shades indicate more soil (Fig.6(c)) with the brighter shades indicating the presence of water. (b) (c) (d) (e) Boundary of Can Tho City Figure 4. Satellite data for Can Tho City, Vietnam. (a) 1979, (b) 1989, (c) 1997, (d) 2002, (e) 2007.

5 URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES 151 (a) (b) (c) Figure 5. VSW triangle with the scatter gram. P: pixel have the same value of Red and NIR wavelenghs. W, V, S: projection of P on edges of triangle. Table 2. Parameters Parameters of vegetation, soil and water lines of triangle. Figure 6. Resulting images. (a) PV image, (b) PS image, (c) PW image. Remote sensing analysis has been created by taking the images of the years 1972, 1989, 1997, 2002 and 2007 to identify the pattern of land cover change that is characterized by various types of land cover illustrated by the seven categories of Region of Interest (ROI), namely: built-up, dry soil, wet soil, dry vegetation, wet vegetation, water, and cloud. The mean values of the indices PV, PS and PW were then plotted in Fig.7 for comparison. As observed from Fig.7, it is difficult to separate the mean values of indices PV, PS and PW for the years 1972 and 2007 in the ROI particularly for the built-up areas and dry soil categories. This means the urban line and dry soil line in Fig.7(a) and Fig.7(e) are not so easy to discriminate when compared to the other classes. In order to overcome this difficulty it was decided to use NDVI to distinguish between built-up areas and dry soil classification NDVI & SVM As just noted, the VSW index had certain limitations in classifying effectively the built-up and dry soil categories; therefore NDVI was considered to enhance class separability. The NDVI (Rouse et al., 1974) values were calculated and are presented in Table 3. In general, the higher values of NDVI are considered representative of denser or more active vegetation, while very low values symbolize dry or non-vegetated regions (Japanese Association on Remote Sensing, 1993). The mean values of NDVI for different land cover classes for the years 1972 and 2007 were determined and represented in Fig.8. It is observed from Fig.8 that the mean values of NDVI enables better separation of built-up areas from dry soil cover. Thus, in order to enhance the accuracy of the land cover classification, we did not limit ourselves to using the VSW index as suggested by previous researchers. We also used the NDVI modified scheme of parameters was proposed for enhancing class separability for the DT classifier. Further, the SVM method was adopted for classifying the land cover and was compared with the results of the DT classifier to cross-check the accuracy. The SVM is an algorithm based on statistical learning theory. According to Huang et al. (2002), and Shafri and Ramle (2009) the SVM gives a more stable overall accuracy than the

6 152 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar maximum likelihood, neural network and DT classifiers. In the present study, SVM produced higher accuracy than DT classifiers with the details described in the next section. Figure 7. Mean values of the ROI of land cover categories using three images PV, PS, PW. (a) 1972, (b) 1989, (c) 1997, (d) 2002, (e) respectively as shown in Fig.9(a) and Fig.10(a). The DT classifier compares the data with a range of carefully selected features. The selection of features was done on the basis of spectral distributions or separability of the classes (Japanese Association on Remote Sensing, 1993). To implement this classification method, the mean radiance values of the indices PV, PS and PW besides NDVI were selected year wise and have been presented in Table 3. The values given in Table 3 were based on the VSW index and NDVI was used in extracting the built-up areas. The urbanization pattern of the two scrutinized areas based on DT classified is shown in Fig.9(b) and Fig.10(b). The black areas show built-up areas, while the remaining light areas are other classes. SVM is a form of machine learning method (Cortes and Vapnik, 1995; Burges, 1998) which is used for classification and regression, for example, two kinds of data sets are in n-dimensional space, SVM constructs a linear surface then creates optimal separating hyperplane which maximizes the distinction between the two data sets (Fig.11). In this study, the hyperplane was chosen in such a way that the distance from the nearest data point on each side would be maximized (optimal distance). It is therefore of interest to know that such a hyperplane exists in the present case and is called the maximum-margin hyperplane. The SVM was applied using ENVI 4.6 software (ITT Visual Information Solutions, 2009). Using a pattern recognition algorithm for the study area, it was possible to effectively classify satellite datasets. The input data were retrieved from the VSW index and NDVI images with the same ROI of seven classes: built-up areas, dry soil, dry vegetation, wet vegetation, water, wet soil and cloud. The classification done by using the SVM method reflecting built-up and other areas are represented in Fig.9(c) and Fig.10(c). Figure 8. Mean values of seven land cover categories of NDVI images. (a) 1972, (b) Table 3. Look-up table for DT classifier method to extract built-up areas Classification In order to examine the suitability of the classification methods namely, DT and SVM and to compare the results, two test areas in urban and in rural environments were chosen. The two areas examined were 375m 285m and 1005m 870m Accuracy assessment The results of built-up areas detected in December 2002 and February 2007 using DT and SVM classifiers were evaluated. They were then compared with an IKONOS image acquired in June In order to extract the urban information of the IKONOS image, a grid of resolution 15m 15m was used as an overlay. Subsequently, individual pixels were marked as built-up were on the basis of visual examination of the IKONOS images (Fig.12). The coded grid was then overlaid on the resulting builtup area map obtained by DT and SVM classifiers in order to compare the accuracy of the results. The overall accuracy of the SVM classifier (71.38%) in Table 5 was higher than the DT classifier (65.86%) in Table 4. Because of the slightly better accuracy, the SVM classifier was used to analyze Can Tho City. The other main advantage of using the SVM classifier, apart from a higher accuracy (in this

7 URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES 153 (a) (a) (b) (b) (c) (c) Figure 10. Test area 2 with built-up areas in black. (a) Test area 2 based on IKONOS image, (b) DT chassifier, (c) SVM classifier. Figure 9. Test area 1 with built-up areas in black. (a) Test area 1 based on IKONOS image, (b) DT chassifier, (c) SVM classifier.

8 154 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar case, at least), is the need of the information to create a look-up table becomes redundant. Figure 11. Optimal separating hyperplane and optimal distance. H1 does not separate data sets, H2 with small margin, H3 with maximum margin. Table 4. Accuracy of DT classifier method. Figure 12. Grid image with 15m resolution based on IKONOS (RGB- 432) in (a) IKONOS in 2008, (b) howing hatched pixels indicating builtup area. Table 5. Accuracy of SVM classifier method. 5 Discussion Urban evolution of Can Tho City and temporal trends in the development In order to collect accurate and time-series information on the conversion of natural land into urban land cover, satellite remote sensing technology was employed to study the Can Tho City area of Vietnam. The SVM classifier using the VSW index and NDVI images used to strengthen the results suggest that urban expansion from 1972 to 2007 was in the north-western direction along Hau River and Highway No. 91 (Fig.13) and agrees with the Master Plan proposed in In contrast to traditional ground-based surveys, satellite remote sensing provides a greater amount of consistent information on the geographic distribution of land-use with relatively less cost and time than by in-situ methods. In addition, the retrospective views of the satellite images permit retrospective surveying of land-use, where alternative data may be lacking. Because the 80m resolution of the 1972 satellite image was low as compared to the 30m resolution of the 1989 image, the built-up areas seems to have reduced from 1972 to 1989 (Fig.13). In reality however, there was little built-up areas change in Can Tho City from 1972 (Fig.14(a)) to 1989 (Fig.14(b)) amounting to nearly a seventeen year period of little urbanization. Although in December 1986, Vietnam entered the Economic Renovation Policy ( Doi Moi in Vietnamese language), and except for a few areas along the Can Tho and Hau Rivers that became built-up areas, the other parts were mainly rural. Subsequent to this, from 1989 (Fig.14(b)) to 1997 (Fig.14(c)), urbanization progressed mainly in the northwestern and partially in south-western direction, with some lesser expansion to the south-west. Because of constraint on expansion caused by the location and alignment the Can Tho River, urbanization in the Cai Rang District (on the right bank of the river) could not be sustained. During the subsequent period of 1997 (Fig.14(c)) to 2002

9 URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES LEGEND Boundary Built-up areas Dry vegetation Wet vegetation Dry soil Wet soil Water Unknown Figure 13. Land cover maps of SVM classifier method. (Fig.14(d)), Can Tho City demonstrated a clear urban expansion. This change was mainly to the north-west but also, to lesser degree, to the south-east (along the Hau River) and south-west (along the Can Tho River and National Highway No.1). After the year 2000 there is accelerated urban development in the areas between Ho Chi Minh City (to the north-east) and Can Tho City, possibly due to the construction of the new My Thuan Bridge that facilitated road access between the two centers. It is also interesting to note that rapid urbanization occurred in the last five years from 2002 onwards (Fig.14(d)) up to 2007 (Fig.14(e)) suggesting a possible impact in the expansion of Can Tho Port and industrial parks. It is also clearly visible from Fig.14(e) that the areas along the O Mon River, Cai Rang River and Can Tho River in the Phong Dien District, as well as the center of the Thot Not District, have become hot spots for urban expansion. Additionally, the south-eastern part of the city has undergone a noteworthy urban increase owing possibly to the construction of the Can Tho Bridge in the area in Spatial Urban Expansion Looking at the entire period of the study starting from 1972 to 2007, changes seem to have mainly occurred in north-western and south-eastern directions. According to the Master Plan (Nguyen Thiem, 2004) future urban population growth was anticipated be confined more along this direction. The large Hau River and the main Highway No. 91 (Fig.2), which are likely to connect Cambodia with Can Tho Port and Dinh An Port in the next few years are also along the same direction. Furthermore Highway No. 91 transects An Giang and Hau Giang provinces which in the future will become important for increasing inter-

10 156 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar (a) (d) (b) (e) (c) LEGEND Built-up areas Rivers Roads District boundaries Significant areas Figure 14. Built-up areas in Can Tho City. (a) 1972, (b) 1989, (c) 1997, (d) 2002, (e) as reference for prioritizing areas to implement suitable infrastructural as well as for civic projects. The second most important direction in which land cover changes occurred is the south-west region, which has also folnational trade between Cambodia and the Vietnamese Mekong Delta (especially in Can Tho City). Thus, future expansion in this area will require proper urban developmental planning. The planners and decision makers can build upon the present study

11 URBAN EXPANSION OF CAN THO CITY, VIETNAM: A STUDY BASED ON MULTI-TEMPORAL SATELLITE IMAGES 157 lowed the Master Plan of This is the region in which urbanization has been developing following National Highway No. 1 (Fig.2) that links Ho Chi Minh City to the provinces of Mekong Delta. The triangle formed by Can Tho City, Ho Chi Minh City and Cambodia is expected to become the focus for significant development. In contrast, the present study reveals that in the western direction towards Kien Giang province (Fig.2) the rate of urbanization has been very low possibly due to the fact that the Master Plan of 2004 has not designated this area for development. However this area has better potential for urban development as the distance between Can Tho City and Kien Giang port is only 100 km by Highways No. 91 and 80. Kien Giang province, which has five ports (Hon Chong Port, Rach Gia Port, Ha Tien Port, Duong Dong-Phu Quoc Port and Tac Cau Port), are all connected directly with Thailand, Cambodia and Malaysia via Gulf of Thailand. Besides this, the coastal zone of Kien Giang province has a high tourist potential. All these add to the favorable conditions for expansion westward towards Kien Giang province. Figure 15. Urban growth rate of Can Tho City, Vietnam. Table 6. Growth in built-up areas distribution. Table 7. Land cover change to built-up areas The rate of urban expansion The assessments of urban area expansion are summarized in Table 6 and Fig.13 for the entire period from 1972 to The rate of expansion from 1989 to 1997 was 8.1%, followed by 22.6% for and by 24.3% for (Table 6, Fig.15). However, expansion before the second image acquired in 1989 was not clear as the 1972 image as it was hampered by cloud cover. The built-up areas changes each time period it is calculated as shown in Table 7. It is observed from Table 7 that the classification categories are bare land (dry and wet soil), vegetated land (dry and wet vegetated), water and unknown (cloud and shadow). However, because of season challenges, what might be vegetated land in one image might be bare ground in another image. Thus, an area that did not change land cover or land-use (but merely became dormant during the dry season), and another area that might have become urbanized (e.g. cleared of vegetation for another purpose) might appear similar in the imagery. Moreover, the percentage of surface water converted to the built-up category was 15.3%, most notably for the period Such conversion could be a significant contributing factor for the observed recent increase in flood events in Can Tho City. (Table 6) for Interestingly, comparison of study results and official statistics at district level showed that for Ninh Kieu, Binh Thuy and Cai Rang Districts, which are dense residential and commercial areas, there no disparity between study results and government statistics. However, the results of the rural districts, O Mon, Phong Dien, Co Do, Vinh Thanh and Thot Not, differed when compared to government statistical data (Table 8 and Fig.16). The possible reasons for this discrepancy could be a) that remote sensing images cannot recognize small areas because of the maximum resolution of 15m, b) some structures Table 8. Urban Built-up area results of districts within Can Tho City. Comparison of 2002 and 2007 with land-use statistics of Comparison of results with government statistical data The findings from this study were compared with the government of Vietnam, statistical records in 2004 (Nguyen Thiem, 2004). According to the records, the buit-up areas of Can Tho City is around h. In contrast, our study indicated the area under built-up land-use was 9.07h for 2002 and h

12 158 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar Figure 16. Comparison of classification results with government statistical data. in rural areas were at least partly covered by vegetation or c) according to the government statistical yearbook, areas that have been official been designated as urban, but which have not yet urbanized but planned to be in the future. Examples of the last could include parks and open space within the areas officially delimited as urban areas which are proposed to be urbanized as a future developmental strategy. 6 Conclusions In the present study an attempt to detect urban change in the Can Tho City of Vietnam by using available multi-temporal satellite images (Landsat MSS, TM, ETM+, JERS-1 and ASTER) from 1972 to 2007 has been made. The challenges of finding suitable for cloud-free imagery, and changes in operating satellites over the 35-year period of the study necessitated this selection of imagery sources. Urban expansion in Can Tho City and temporal trends in its urban development were evaluated to better understand the city s evolution during different portions of this period. The Vegetation-Soil-Water (VSW) index was applied to overcome the different digital image characteristics of various satellites used. Additionally, the NDVI was adapted for enhancing discrimination of the perceived land cover classes. For classification purposes, the SVM technique was found to be better than the DT method that was used by previous researchers such as Huang et al. (2002), and Shafri and Ramle (2009). The study revealed that the extraction of built-up areas by combining the VSW index with the NDVI was more effective in discriminating dry soil and built-up areas. Further, with the help of the SVM classifier it was possible to obtain higher accuracy than using the DT method. This research suggests that the satellite image classification estimates possibly characterize areas presently under urbanization more accurately than the official records which incorporated areas that are planned for future urban development. The results obtained indicated that the north-western region was the major direction of urban expansion of Can Tho City during the last 10 years. However, relatively recently urban expansion has been more prevalent to the south-east and southwest particularly in Phong Dien and Cai Rang Districts. These developments are also included in the 2004 Master Plan for Can Tho City. Hence, these regions may become important urban areas and benefit from timely assessment for prudent social, economic and environmental planning. In general, the central districts have the highest population densities ranging from 7-19 times larger than in rural districts, possibly due to heterogeneous urban infrastructure. In the study, the western direction is suggested for future urbanization because of anticipated advantages of international trade due to proximity to Thailand, Cambodia and Malaysia, and also due to the potential for increase tourism in Kien Giang province. The central areas of Can Tho City in Ninh Kieu District present a high rate of urbanization. Apparent associated silting of streams and infilling of other water bodies may have contributed to an increase in floods in that region. Considering these challenges, the study suggests that further research assessing which areas are most likely to be affected by floods should help in proposing solutions for better mitigation and management of flood situations. This may require the acquisition and use of high resolution satellite images, and enhanced implementation of classification procedures for urban land-use change. Acknowledgements The first author Pham Thi Mai Thy would like to express sincere thanks to the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan for providing at Japanese Government (Monbukagakusho) Scholarship. Thanks are also to Osaka City University for providing research facilities and financial support for the purchase of the data. The help and support obtained from the authorities of Ho Chi Minh City Institute of Resources, Geography, GIS and Remote Sensing Research Center are thankfully acknowledged. The authors are also grateful to Prof. Shinji Masumoto, Prof. Michelle Graves, Dr. David Hastings, Ms. Daniele Nascimento, Ms. Saroj Akoijam and Mr. Daisuke Yoshida for their support and encouragement. References Burges, C. J. C. (1998) A tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, vol. 2, pp Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, vol. 20, no. 3, pp Duong Quang Nghi (2008) Urbanization without sprawl - Viet Nam experiences and perspective. 44th ISOCARP Congress 2008, 4p. Farooq, S. and Ahmad, S. (2008) Urban Sprawl Development

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14 160 Pham Thi Mai Thy, Venkatesh Raghavan, N.J. Pawar * * N. J. ** VSW NDVI SVM Huang 2002 Shafri et al DT VSW SVM NDVI * **