Modelling and Predicting Multiple Land Use/Land Cover Changes Using Logical Regression Method and Markov Chain with Choice Approach Among Existing Options

The study of changes and the rate of destruction in previous years, as well as the possibility of predicting these changes in recent years, plays an important role in optimal planning and management and limiting unusual changes in the future. The study was conducted to identify land changes and land cover (1996-2006) and to predict future changes (2026) using satellite imagery in the Massachusetts region of the United States using a logical regression method. To increase the efficiency and optimization of the model, the efficient features have been selected using Cramer's V test. The study area is classified into 18 classes, which as a result of the analysis showed a decreasing trend of 3.84% by grasslands, while bare lands have increased by 9.78% and unconsolidated shore by 9.5%; as well as other land uses have changed slightly between 1996 and 2006. Accordingly, Land-use/Land-cover changes have been predicted using the logical regression method and Markov chain model by selecting from the available options using the Cramer's V test for 2026. The accuracy of this method using the 2016 data had an overall accuracy of 84.21% and a Kappa index of 0.7150. The results of this study showed rapid changes in land use for future years. The conversion of forest land into other lands, especially grassland and pasture, was the most important change in land cover in the future. Therefore, planning for the protection and restoration of the forest should be a key plan for decision-makers in the study area.

Modelling and Predicting Multiple Land Use/Land Cover Changes Using Logical Regression Method and Markov Chain with Choice Approach Among Existing Options Keywords:

Land-use Change Prediction , Logistic Regression , Cramer’s V , Markov Chain