Monitoring land use change processes in Kerman based on Markov model and satellite data

Abstract

Understanding the reasons for land use change over time is necessary for optimal planning and management of urban areas. Identification of alterations and the patterns they reflect allows prediction of future changes, and appropriate planning can then be undertaken. In the last two decades, the city of Kerman, Iran, has gone through several identifiable changes, including significant increases in its urban population and growth of its urban residential areas. As a result of population increase, provision for Kerman’s human needs, which requires extensive use of natural resources, will undoubtedly increase the demand for land resources, both in agricultural and non-agricultural sections. Due to the fact that development without planning leads to inappropriate use of lands and resources, the present study was set to contribute in planning through an analysis of land use changes in various areas of Kerman city between 1989 and 2017, and to predict probable changes during the next ten years through performing visible and near infrared data of Landsat TM/ETM+/OLI. Classification of land use classes, and analysis of methods and their changes, was carried out by ENVI 5.1 and IDRISI 17 software. Maximum likelihood method was selected for Image classification. Classified images were applied as input for Land Change Modeler (LCM). Results indicated that by 2017, LCM had approximately doubled, bare surface had decreased, and vegetation had significantly increased. Furthermore, the results of Cellular Automata (CA) Markov method in 2017 showed that according to the predictions the area of bare surface and vegetation have respectively decreased by 118 and 219 hectares and the area of built-up land had not changed significantly when compared with the base year 2017. This research is anticipated to help local managers better view on the addressed land use system for improved land use management strategies upon urban expansion balance.