Urbanization Dynamics and 2050 Land Use Simulation in Ao Nang, Krabi: A CA-Markov Approach - Katawut Waiyasusri; Nanthaporn Usaard; Dolreucha Suwanmajo (Journal of Multidisciplinary Applied Natural Science, 2026)
Ao Nang, a coastal tourist city in Krabi Province, has been developing its area to support both Thai and foreign tourists since 1983. This research selected Landsat 1 satellite imagery from 1973, which was before tourism development, to conduct this research. The objective of this research is to analyze the changes in land use patterns between 1973 and 2025, and to model future land use using the CA-Markov model in Ao Nang. The results found that in 1973, the city, town, and commercial areas covered an area of 9.38 km2 (7.82% of the total area), and in 2025, the area increased to 21.95 km2 (18.31% of the total area). Urban expansion has affected other types of land use, such as evergreen forest and various agricultural areas, such as para rubber and mixed orchard. During 1973–2025, urbanization has occurred in important beach areas, such as Nopparat Thara Beach, Ao Nang Beach, Railay Beach, Tubkaek Beach, and Khlong Muang Beach; and important highways along the line, such as 4201, 4203, 4034, and 6024. The results of the Cellular Automata Markov Model (CA-Markov Model) show the simulation of future land use, which occurs in 2050. The CA-Markov model predicts that urban and built-up areas will expand significantly to 40.32 km2 by 2050, representing an 83.7% increase from the baseline. Conversely, forest cover and agricultural land are projected to decline by 18.78% and 12.72%, respectively. The results show that in the next 25 years, the city, town, and commercial areas will expand the most, followed by shrimp farms. If land use changes occur without control, it may affect the use of such land to increase rapidly. However, the research results this time have shown land use data in spatial form that is useful for planning land use development in the coastal tourist city of Ao Nang effectively. Furthermore, this information will support decision-making in preparing for spatial development for sustainable tourism.