Prediction of Greater Bangkok expansion using nighttime light image and CA-Markov Model

Abstract

This study explores land-use changes in Greater Bangkok between 2013 and 2027 using nighttime light (NTL) data and CA-Markov modeling. NTL imagery from Suomi NPP VIIRS facilitates analysis of urban expansion trends. Unsupervised K-means classification categorizes land use into high-density urban, urban, and non-urban areas. Key findings reveal a continuous increase in high-density urban areas (0.08% annually) and a slight expansion of the urban areas (0.01% annually). Conversely, non-urban areas are decreasing (-0.02% annually), suggesting potential conversion to urban use. The CA-Markov model, informed by NTL data, predicts land-use changes for 2027. Predictions indicate a continuation of observed trends, with high-density urban areas experiencing the most significant growth. This study highlights the potential of NTL data and CA-Markov modeling for urban sprawl analysis and future land-use prediction. However, limitations include the spatial resolution of NTL data and the Markovian assumption of the CA-Markov model. Future research should incorporate additional NTL data, explore integration with other relevant datasets, and investigate alternative modeling approaches.