Integrating logistic regression and cellular automata-Markov models with the experts' perceptions for detecting and simulating land use changes and their driving forces.

Modeling spatial-temporal dynamic of land use change is of great necessity for understanding the status of the past, causes of the change, and prediction of the future. This study aims to objectify three topics which include identifying the past land use changes, modeling the future changes, and subsequently considering their driving forces. The change detection analysis has shown that about 12,081.

Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran.

The study of changes and destruction rate in the previous years as well as the possibility of prediction of these changes in the following years has a key role in optimal planning, controlling, and restricting non-normative changes in the future. This research was approached to detecting land use/cover changes (1985-2007) and to forecast the changes in the future (2021) use of multitemporal satellite imagery in semi-arid area in western Iran. A supervised classification of multilayer perceptron (MLP) was applied for detecting land use changes.