Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach

Abstract

This study used the postclassification change detection approach to identify land use land cover changes in Norman, Oklahoma, between September 1979 and July 1989 using Landsat multispectral scanner and thematic mapper (TM) images. An integration of Markov chain analysis and a cellular automata approach was employed to predict land use land cover of Norman in 2000 using multicriteria decision-making and fuzzy parameter standardization approaches. Accuracy assessment was carried out using a stratified random sampling technique. The identified random sample points were displayed on Landsat enhanced thematic mapper (ETM) image data acquired on 22 May 2000, with the help of local area knowledge, ground information collection, and existing land use maps of Norman to identify the classes. We also directly compared projected results against the classified output of the same Landsat TM image. This study demonstrates the usefulness of Markov and cellular modeling for urban landscape changes. A checklist of the sources of limitation or uncertainty in the application of this approach is also reported.