Land Cover Change Modeling Using a Hybrid Approach and Remote Sensing Data

Wetlands are vital for human survival and constitute 40.6% of the total value of global ecosystem services. This study analyzed land cover changes in the Golpayegan Shoor wetland from 1988 to 2018 and then modeled it with a combination of artificial neural network, Markov chain, and multi-objective optimization for 2030 and 2040 for obtaining environmental sustainability. To obtain the highest accuracy in transition potential modeling, four scenarios were used with various calibration periods and sub-models. The FOM index was used to evaluate the accuracy of various scenarios using the approach by overlaying three maps for the years 2008, actual 2018, and predicted 2018 maps. Effective variables in describing the changes occurred in the case study selected using Cramer's v. The highest accuracy was shown in a scenario with the calibration period of 1988-1998 and 9 sub-models in land cover change modeling 2018 year. The evidence likelihood of change map and distance from agricultural lands showed the highest impact in the changes of the study area. Residential areas and agricultural lands increased by 0.6% and 1.8% in 2030, while 1% and 2.2% grew up in 2040 compared to 2018, respectively. Dense rangeland decreased by 13.1% and 18.6%, respectively in 2030 and 2040 compared to 2018. The water bodies showed an increase of 0.18% and 0.27% due to the conversion of agricultural areas and rangeland to Lake Golpayegan and Kochery dams. Taking water from unauthorized wells, reducing the flow of water entering the wetland, and the presence of decorative ore has caused serious problems for Golpayegan Shoor Wetland, which requires managers and land planners to consider compensatory ways to restore, water requirement, and ecological sustainability for the wetland.