DETECTION AND ANALYSIS OF LAND-USE/LAND-COVER CHANGES IN NAY PYI TAW, MYANMAR USING SATELLITE REMOTE SENSING IMAGES

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1 DETECTION AND ANALYSIS OF LAND-USE/LAND-COVER CHANGES IN NAY PYI TAW, MYANMAR USING SATELLITE REMOTE SENSING IMAGES Kay Khaing Oo 1, Eiji Nawata 1, Kiyoshi Torii 2 and Ke-Sheng Cheng 3 1 Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University, Kyoto, Japan, kkhaing1@gmail.com; nawata@kais.kyoto-u.ac.jp 2 Centre for Southeast Asian Studies, Kyoto University, Kyoto, Japan, torii-k@maia.eonet.ne.jp 3 Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan. R.O.C, rslab@ntu.edu.tw KEY WORDS : Agriculture, Urbanization, Post-classification change detection ABSTRACT : Nay Pyi Taw has experienced significant Land-use and land-cover changes since its becoming the capital city of Myanmar in The city was once a greenfield site in the shrub land and new developments have been underway since Detecting land-use/land-cover (LULC) changes in the city and its vicinity and understanding the impact of such changes are of vital importance for future planning of the city. In this study, multispectral satellite remote sensing images of the survey area acquired over the period from 2000 to 2014 were used for the detection and analysis of LULC changes in Nay Pyi Taw. For analysis of change detection, postclassification change detection method (with land use/cover classes such as agricultural land, natural vegetations, water, settlements/built-up and bare land), were applied in this study. From the results of change detection, the changes of LULC in different categories were analyzed. The area of the settlement/build-up increased approximately ha while agricultural land decreased ha and natural vegetation decreased ha. The area of water bodies also increased ha. Bare soil area increased ha that means the overestimation of the bare soil according to the low user's accuracy of bare soil in INTRODUCTION Myanmar is an agricultural country and its economy largely depends on agriculture. It provides livelihood to about 70 percent of the population. Agricultural area expansion is important for increasing crop production and sustainable development of agriculture. However, some agricultural lands were converted to urban lands due to the urbanization process. The study area, Nay Pyi Taw has experienced significant land-use and land-cover changes since its becoming the capital city of Myanmar in Urbanization is one of the most widespread causes of the loss of agricultural lands, habitat destruction and the decline in natural vegetation cover. It is necessary to monitor land use change and land cover information to investigate how much urbanization impacts on the agricultural lands, natural vegetation cover and environment. It is also important to determine the trend and rate of land cover conversion for the development planner to establish balanced land use policy. For this purpose, remote sensing is very effective tool. The temporal dynamics of remote sensing data can play an important role in monitoring and analyzing land cover change even in past years. Many studies have discussed land cover and land use change for different purposes by using remotely sensed data (Babykalpana & ThanushKodi, 2010; Ma, Wang, Veroustraete, & Dong, 2007; Yuan, Sawaya, Loeffelholz, & Bauer, 2005). In this study, multispectral satellite remote sensing images of the survey area acquired over the period from 2000 to 2014 were used for the detection and analysis of LULC changes in Nay Pyi Taw. Post-classification change detection method was applied to detect the changes in five major classes (agricultural land, natural vegetation, water, settlement/built-up and bare land). The objectives of the study are: (1) to examine the spatial distribution patterns of various land covers at different times. (2) to find out the information on land use and land cover changes. 2. STUDY AREA The study area is located in the central Myanmar. Geographically it lies between 19 º 25' 39 N and 20 º 19' 31" N and 95 º 43' 47" E and 96 º 36' 49" E. Total geographical area is approximately 7,054 km². Nay Pyi Taw (NPT) was established in 2004 as an administrative center. Formerly, Yangon was the capital of Myanmar and all of the Ministries were located in Yangon. As Yangon has become too congested and populated, government decided to establish a new administrative capital. On 6 November, 2005, the administrative capital of Myanmar was officially moved from Yangon to Nay Pyi Taw. So, it has significant Land-use and land-cover changes. Formerly, most of areas in Nay Pyi Taw were covered with natural vegetation and cultivated crops. Nowadays, all ministries, administrative offices, airport, expressway, new residential areas, recreation centers and hotel zone are well

2 established for urbanization. There are 8 townships in the study area _ Tatkon, Pyinmana, Lewe, Oke-ta-ra-thi-ri, Poke-ba-thi-ri, Zay-yar-thi-ri, Det-khi-na-thi-ri and Za-bu-thi-ri. Pyinmana, Lewe and Tetkon townships were formerly part of Yamethin district. Oke-ta-ra-thi-ri, Poke-ba-thi-ri, Zay-yar-thi-ri, Det-khi-na-thi-ri and Za-bu-thi-ri are new townships after establishment of new capital, Nay Pyi Taw (Figure-1). Study Area (Nay Pyi Taw) Yangon Figure 1. Location of study area divided by 8 townships in Myanmar as a Landsat 8 (OLI) image of 2014 (in RGB combination of bands 6, 5 and 4) 3. MATERIAL AND METHODS There are two main steps were involved in this research which are classification of the satellite data for LULC and the change detection analysis in the LULC types. Image registration, image fusion, visual interpretation, classification, and change detection using post classification comparison were involved in the satellite data analysis Data Acquisition To detect the land use and land cover change, satellite imageries of two different times were procured: Landsat 7 ETM+ of April 4, 2000 (before NPT) and Landsat 8 OLI of April 3, 2014 (after NPT). The two satellite images acquired in April were used because they are cloud-free images and irrigated summer paddy is grown in that season. The study area is contained within the Landsat path 133, row 46. All images are the Level 1T (L1T) data products provided systematic radiometric accuracy, geometric accuracy by incorporating ground control points, while also employing a Digital Elevation Model (DEM) for topographic accuracy. Obtained images have been registered to the UTM map projection with a datum of the WGS84. For the reference data of the 2000 image, 22 sheets of

3 Topographic maps (scale 1:50,000) produced by the Survey Department, Ministry of Forestry, Myanmar in 2002 are used. Administrative boundary of study area, Nay Pyi Taw (shape file) provided by is applied to subset the study area from the whole Landsat scene Preprocessing Image-to-image registration : From the original two full Landsat scenes, a sub-area covering the study area was selected. In order to analyze the Landsat data from the two dates, they need to be geometrically registered to each other (Q. Zhang, Wang, Peng, Gong, & Shi, 2002). The 2014 image was registered to the 2000 image using a first order polynomial function. A total of 9 control points were collected with a RMS error of 0.27 pixels. Image fusion and visually delineation of settlement areas : Image fusion, also called pan-sharpening, is a technique used to integrate the geometric detail of a high-resolution panchromatic (Pan) image and the color information of a low-resolution multispectral (MS) image to produce a high-resolution MS image (Y. Zhang, 2004). In this study, image fusion was individually carried out on Landsat7 ETM+ image and Landsat 8 OLI image using multispectral bands (30 m spatial resolution) and panchromatic band (15 m spatial resolution) to improve the spatial resolution of multispectral image. By using those pan-sharpening images, the manually delineation of settlement areas was done because settlement areas were heterogeneous areas including trees, undeveloped lands, old buildings and new buildings. Therefore, their spectral signatures were varies and easily confused with harvested agricultural lands and deciduous natural vegetation while applying the digital classification. For this reason, settlement areas were manually delineated and masked out from the digital classification of the images. Visual interpretation was carried out by using the man-machine interactive visual interpretation method in which the image can be zoomed in, zoomed out, moved and enhanced on the software platform and the border line directly can be outlined with the mouse (Ghorbani & Pakravan, 2013). Image fusion (or pan-sharpening) techniques have proven to be effective tools for providing better image information for visual interpretation, image mapping, and image-based GIS applications (Y. Zhang, 2004) Change Detection For the analysis of the change detection, the post-classification method was carried out. This method requires both images to be individually rectified and classified before compared pixel by pixel. The eight land use/cover classes (deciduous natural vegetation, evergreen natural vegetation, irrigated paddy field, cultivated agriculture, harvested agriculture, water and bare soil) were classified according to the spectral signatures of land-cover on the satellite images. As the images acquired in April, summer season in Myanmar were used for this study, deciduous natural vegetation was leaf-fall that causes the spectral signature different from evergreen natural vegetation. And some areas grow irrigated rice, some agricultural lands were cultivated and some were harvested. Using the level 1 classification scheme developed by (Anderson, Hardy, John, & Richard, 1976), these eight land use/cover classes were classified on each Landsat image using Maximum Likelihood supervised classification. Settlement areas were manually delineated with the help of pan-sharpening. Table 1 shows the description of different land use/cover classes of the study area. For the reference data of the 2014 image, ground truth points were used for the training samples as well as the test samples for the validation of the accuracy. For the reference data of the 2000 image, 22 sheets of Topographic maps (scale 1:50,000) produced by the Survey Department, Ministry of Forestry, Myanmar in 2002 were used. Some checks of reference data were done with the help of Google earth. Finally, some of them were combined into 5 major land use/cover classes: Natural vegetation, Agricultural land, Water, Settlement/builtup and Bare soil. The land use/cover maps from two different periods were used for post-classification comparison which is the most commonly used quantitative method of change detection with fairly good results (Dewan & Yamaguchi, 2009; Mallupattu & Sreenivasula Reddy, 2013; Reis, 2008). Table 1. Description of different land use/cover classes of the study area Class Natural vegetation Agricultural land Water Settlement/built-up Bare soil Description Evergreen forest land, deciduous forest land, shrub and bamboo Areas cultivated with annual crops, rainfed-rice, irrigated rice and orchard Permanent open water, lakes, reservoirs and streams Commercial, and residential areas, and other areas with man-made structures: roads, railway lines, recreation areas, golf courses, parks and ponds. Sandy soil that cannot be grow plants, especially near river banks

4 4. RESULTS AND DISCUSSION 4.1. Accuracy of Visual Interpretation For the manual delineation of the settlement/built-up areas, visual interpretation was carried out with the help of high- spatial resolution pan-sharpening images. The boundaries of the settlement/built-up area can be clearly delineated. The accuracy of visual interpretation was checked by using topographic maps, Google earth and ground truth points collected by GPS Classification Accuracy Assessment Error matrix that is the comparison of a classification with ground-truth data to evaluate how well the classification represents the real world was used to assess classification accuracy (Congalton, 1991; Foody, 2002). Overall accuracy, user's accuracy and producer's accuracies, and the Kappa statistic were then derived from the error matrices. Table 2 shows the summary of Landsat classification accuracies (%) and Kappa statistics for the land use/cover maps of 2000 and The overall accuracy for 2000 and 2014 were, 95.21% and 95.69% respectively, with Kappa statistics of 0.93 and User's and producer's accuracies of individual classes, except Bare soil were consistently high, ranging from 79.61% to 99.8%. User's accuracy for Bare soil in 2014 was low at 47.79%, meaning that there is a probability that pixels classified as Bare soil may not actually exist on the ground. As April is summer season in Myanmar, some agricultural lands were harvested and the spectral signature of that class was confused with bare soil. Figure 2 shows Land use/cover maps of the year 2000 and Table 2. Summary of Landsat classification accuracies (%) and Kappa statistics for 2000 and 2014 LULC category 2000 Accuracy 2014 Accuracy Producer's User's Producer's User's Deciduous natural vegetation Evergreen natural vegetation Irrigated paddy field Cultivated agricultural land Harvested agricultural land Water Bare soil Overall accuracy 95.21% 95.69% Kappa statistics Figure 2. Land use/cover maps of the year 2000 and 2014 derived from Landsat 7 ETM+ and Landsat 8 OLI images

5 4.3. Change Maps and Statistics The details of area under each category are given below in Table 3. On the basis of analysis of the area for the years 2000 and 2014, several changes are found in land use and land cover of the study region. These changes are found in every land use and land cover class. Figure 3 describes Land use/cover maps of combining eight classes into five classes of the year 2000 and The results show the area of the settlement/build-up increased approximately ha while agricultural land decreased ha and natural vegetation decreased ha. The area of water bodies also increased ha. Bare soil area increased ha that means the overestimation of the bare soil according to the low user's accuracy of bare soil in Figure 4 shows the land use/cover change map from 2000 to Table 3. Area and percentage of different land cover classes of 2000 and 2014 classified images Class name 2000(ha) % 2014 (ha) % Difference Settlement/Built-up % % Agricultural land % % Natural vegetation % % Water % % Bare soil % % Total area % % Figure 3. Land use/cover maps combining into five classes of the year 2000 and 2014 Figure 4. Land use/cover change map from 2000 to 2014

6 5. CONCLUSION The results show that land use/cover classification and change detection using Landsat images provides landscape change maps and statistics. The results from classifying the amount of land in the study area indicate that some area of agricultural lands and natural vegetations converted to urban use during the period from 2000 to 2014 due to the establishment of the new administrative capital named Nay Pyi Taw that started in Some of the natural vegetation area also converted into water due to the construction of new reservoirs. The results quantify the land cover change patterns in Nay Pyi Taw area and prove the potential of Landsat data to provide an accurate, economical method to map and analyze changes in land use/cover over time that can be used as inputs to land management and policy decisions for sustainable development of agriculture and estimation of urban growth. REFERENCES: Anderson, J. R., Hardy, E. E., John, T. R., & Richard, E. W. (1976). A land use and land cover classification system for use with remote sensor data. USGS Professional Paper, 964, Babykalpana, Y., & ThanushKodi, K. (2010). Classification of LULC Change Detection using Remotely Sensed Data for Coimbatore City, Tamilnadu, India. arxiv Preprint arxiv: Retrieved from Congalton, R. G. (1991). Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), Ghorbani, A., & Pakravan, M. (2013). Land use mapping using visual vs. digital image interpretation of TM and Google earth derived imagery in Shrivan-Darasi watershed (Northwest of Iran). European Journal of Experimental Biology, 3(1), Mallupattu, P. K., & Sreenivasula Reddy, J. R. (2013). Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, 2013, Ma, M., Wang, X., Veroustraete, F., & Dong, L. (2007). Change in area of Ebinur Lake during the period. International Journal of Remote Sensing, 28(24), Reis, S. (2008). Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2-3), Zhang, Q., Wang, J., Peng, X., Gong, P., & Shi, P. (2002). Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing, 23(15), Zhang, Y. (2004). Understanding image fusion. Photogrammetric Engineering and Remote Sensing, 70(6),

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