Modeling urban land use change: Integrating remote sensing with socioeconomic data

Abstract

Rapid urban development has stimulated the progress in predicting and evaluating urban landscape evolution. As a result of rapid socioeconomic development, the land use pattern of Houston, TX, has undergone significant changes over the past 30 years. It is essential to simulate urbanization processes in Houston to examine where and to what extent landscape change has occurred and further to understand how and why the change can occur. This research developed two cellular automata (CA) models based on the same remote sensing data source: one was based on the classification from Landsat images and another one incorporated the socioeconomic data with the same classification results. The predicted results from these two models suggested that the incorporation of socioeconomic data improved the accuracy in human-intervened landscapes, such as residential and industrial/commercial area. More socioeconomic data and finer data sources were needed to improve the CA model to predict the heterogeneous pattern within urban areas.