Abstract
Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.
Highlights
In the past three decades, the cities in developing countries have experienced a rapid increase in the rate of the population growth
The land use and land cover (LULC) simulation models in an urban study are developed from the theories of urban morphology and dynamic process of LULC to forecast the urban expansion in different patterns and scales [11,12]. Those models can be categorized into four types: (i) empirical and statistical models such as Markov Chain (MC), logistic regression, etc. [13,14]; (ii) dynamic models such as cellular automata (CA), agent-based model (AGB), genetic algorithm (GA), artificial neural network (ANN), system dynamic (SD), etc. [15,16,17,18,19,20,21]; (iii) integrated models such as conversion of land use and its effects at small regional extent (CLUE-S) and Dyna-CLUE [22]; and (iv) hybrid models, like Metronamica, land transformation model (LTM), land change modeling (LCM), SLUETH, etc. [23,24,25,26,27,28,29]
In order to understand the LULC changes and dynamics of urban expansion in the Bangkok Metropolitan Region (BMR), this study divides the results into four sections: (i) LULC classification and accuracy assessment from the satellites images; (ii) Analysis of LULC change in the study periods; (iii) LULC prediction and validation; and (iv) LULC model application
Summary
In the past three decades, the cities in developing countries have experienced a rapid increase in the rate of the population growth. Many satellite images have been used by scientists to comprehend the spatial-temporal evolution of an urban area [7] They could not account the need for prediction of future LULC without an incorporation of land use models [8,9]. The LULC simulation models in an urban study are developed from the theories of urban morphology and dynamic process of LULC to forecast the urban expansion in different patterns and scales [11,12] Those models can be categorized into four types: (i) empirical and statistical models such as Markov Chain (MC), logistic regression, etc. To understand the urban expansion phenomena and LULC in BMR, this current study tries to address the complex phenomenon of the urban context by integrating physical, demographic, and economic data into the LULC model to calibrate and simulate the future land use change from the base year 2014. The projected population and economic growth rates are applied to compute the transition probability by extrapolation, MC, and SD to simulate the urban LULC in 2035
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