Abstract
Myanmar is known as a rich land and it is an agriculture-based country. Rice is the staple food of Myanmar and Irrawaddy delta is the main source of rice production in Myanmar. And there was a Nargis Cyclone hit that region in the early May, 2008 and it was extremely severe caused the worst natural disaster in the recorded history of Myanmar. So it is necessary to be a lot more attention on that region in order to analyze the change in environment to support decision making, urban development and planning. The aim of this study is to analyze the environmental changes in Irrawaddy Delta using Landsat satellite images between the years 2000 and 2017. Collection of Landsat images for the years, 2000, 2005, 2010 and 2017 is used to determine changes of environment. Images are classified into five types: water, forest, buildup, fields and others. For land cover index classification, two supervised classification algorithms: Random Forest (RF) and Support Vector Machine (SVM) are utilized and compared the results of classification using producer accuracy and user accuracy. According to the experimental result, the result of RF is more précised than that of SVM for environmental change detection.
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Khaing, S.S., Aung, S.W.Y., Aung, S.T. (2019). Analysis of Environmental Change Detection Using Satellite Images (Case Study: Irrawaddy Delta, Myanmar). In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_40
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DOI: https://doi.org/10.1007/978-981-13-0869-7_40
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