Monitoring the land use/land cover changes and simulation by using the CA-Markov model-a case study in Bang Lamung district, Pattaya city, Chon Buri province, Thailand
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Abstract
The land use/land cover (LULC) changes in Bang Lamung District by using a Cellular Automata-Markov (CA-Markov) model was investigated. The results showed that the first scenario is shown to be spontaneous in LULC change. There has been increased in the built-up areas over the past few decades, with the highest growth since 2009-2032 in the built-up areas with an average annual change of 94.83%. The agriculture areas have been steadily decreasing with an average annual change of -46.41%. The second scenario is shown to be the green area improvement. The study found that the green area would be remained sufficient for the population in 2027 and 2032. However, the annual change rate of green area per population tended to decrease, especially in the areas of Pattaya City such as Nong Pa Lai Sub-district, Bang Lamung Sub-district, and Na Kluea Sub-district. The third scenario is shown to be the area comprehensive town plan. A comparison of the simulation results of the LULC map in 2027 and 2032 with the current Bang Lamung Town Plan found that the urban expansion area has changed according to the overall town plan. The results indicated that the built-up area is expected to increase by an annual change of 13.10%, while the area used for agriculture is expected to decrease by an annual change of -55.99%. The distribution of controlled and protected areas is revealed inconsistent with the town plan due to the inefficiency of government officials. These land use changes will be useful in planning the urban administration and management of Bang Lamung City to accommodate the growth of the EEC project.
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References
Aburas, M. M., Ho, Y. M., Ramli, M. F. and Ashaari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models. A review. International Journal of Applied Earth Observation and Geoinformation, 52:380-389.
Alam, N., Saha, S., Gupta, S. and Chakraborty, S. (2021). Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain. a Geospatial approach. Annals of GIS, 27:299-314.
Arsanjani, J. J., Helbich, M., Kainz, W. and Boloorani, A. D. (2013). Integration of Logistic Regression, Markov Chain and Cellular Automata Models to Simulate Urban Expansion. International Journal of Applied Earth Observation and Geoinformation, 21:265-275.
Barney, C. (2006). Urbanization in Developing Countries: Current Trends, Future Projections, and Key Challenges for Sustainability. Technology in Society, Elsevier, 28:63-80.
Department of Public Works and Town Country Planning (2019). Banglamung Comprehensive Plan. Retrieved from https://www.banglamungcomprehensiveplan.com/
Eastern Economic Corridor (EEC) Office (2019). The Eastern Economic Corridor Project. Retrieved from http://www1.ldd.go.th/WEB_OLP/report_research_E.html
Gong, W., Yuan, L., Fan, W. and Stott, P. (2013). Analysis and Simulation of Land Use Spatial Pattern in Harbin Prefecture Based on Trajectories and Cellular Automata - Markov Modeling. International Journal of Applied Earth Observation and Geoinformation, 34:207-216.
Guidelines for Green Area Management and Green Area Ratio Standards for Urban Communities in Thailand (2017). Sustainable Urban and Community Development under ASEAN Working Group on Environmentally Sustainable Cities. Retrieved from www.onep.go.th
Hyandye, C. B. and Martz, L. W. (2017). A Markovian and Cellular Automata Land-use Change Predictive Model of the Usangu Catchment. International Journal of Remote Sensing, 38:64-81.
Landis, J. R. and Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33:159-174.
Mas, J. F., Lemoine-Rodríguez, R., González-López, R., López-Sánchez, J., Piña-Garduño, A. and Herrera-Flores, E. (2017). Land Use/land Cover Change Detection Combining Automatic Processing and Visual Interpretation. European Journal of Remote Sensing, 50:626-635.
Pielke Sr, R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Hossain, F., Goldwijk, K. K., Nair, U., Betts, R., Fall, S., Reichstein, M., Kabat, P. and Noblet, N. (2011). Land Use/land Cover Changes and Climate: Modeling Analysis and Observational Evidence. Wiley Interdisciplinary Reviews: Climate Change, 2:828-850.
Singh, R. P., Singh, N., Singh, S. and Mukherjee, S. (2016). Normalized difference vegetation index (NDVI) based classification to assess the change in land use/land cover (LULC) in Lower Assam, India. International Journal of Advanced Remote Sensing and GIS, 5:1963-1970.
Soytong, P., Janchidfa, K. and Chayhard, S. (2019). Strategic Environmental Management Plan for Green Growth Development in the Special Economic Eastern Region: Case Study of Chon Buri Green Space Network Master Plan Development. Faculty of Geoinformatics, Burapha University. Chon Buri province, Thailand, pp.204.
Waddell, P. (2002). A Behavioral Simulation Model for Metropolitan Policy Analysis and Planning: Residential Location and Housing Market Components of UrbanSim. Environment and Planning B: Planning and Design, 27:247-263.
Wang, R., Derdouri, A. and Murayama, Y. (2018). Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area. Sustainability 10:2056.
Yulianto, F., Suwarsono, S. and Sulma, S. (2018). Improving the Accuracy and Reliability of Land Use/Land Cover Simulation by the Integration of Markov Cellular Automata and Landform-based Models: a Case Study in the Upstream Citarum Watershed, West Java, Indonesia. Journal of Degraded and Mining Lands Management, 6:1675-1696.
