Assessing and Predicting Land Use Changes Using Object-Oriented Processing and Markov Chain Methods (Case Study: Barandozchai Watershed, West Azerbaijan).

Abstract

One of the main factors in understanding environmental changes on different spatio-temporal scales is examining the prediction of land use changes. This research aimed to investigate the current and predict future landuseland use changes in the Baranduzchai watershed in West Azerbaijan. The Sentinel-2 images were received for 2016, 2020, and 2022, and pre-processing methods were applied to the images, and all processed images were exported to the eCognition software. Based on the object-oriented algorithms, the nearest neighborhood classification was applied, and by operating the CA-Markov method, the land use changes were simulated for 2028. Finally, the accuracy of the final map was validated. The results indicated that it is possible to produce land use maps with a high accuracy (Kappa coefficient 93%) using the nearest neighborhood approach, and the Markov model produced the map of land use changes with an acceptable accuracy (81%). Until 2028, agricultural lands will increase by 30.08%, residential areas by 1.48%, and salt lake areas by 0.02%. Soil class will decrease by 16.24% and pastures by 15.21%. The results of this research can help to evaluate past actions and find solutions to formulate strategies for land management in the Barandozchai watershed