Land Use Changes Modeling and Predictions Using CA-ANN Hybrid Model in‎ Khamir and Qeshm Mangrove Forests

Document Type : Original Article

Authors

1 Postdoctoral student, Department of Environmental Science, Natural Resources Faculty, University of Tehran, Karaj, Iran

2 Professor, Department of Environmental Science, Natural Resources Faculty, University of Tehran, Karaj, Iran

Abstract

Land use changes as one of the most serious human threats have led to the destruction of ‎biodiversity and the reduction of ecosystem services in the Khamir and Qeshm mangrove ‎forests. In this study, the spatial-temporal land use changes in this area were investigated using ‎the Landsat satellite images (1989-2023) in the Google Earth Engine (GEE) web system. In ‎addition, to model and predict these changes, the hybrid model of artificial neural network and ‎cellular automata (CA-ANN), based on the descriptive variables of height, slope, population ‎density, distance from settlements, distance from the city center and distance from the roads ‎were examined and a map of the possible trend of land use changes for 2060 was also prepared. ‎Finally, using the ordinary least squares (OLS) regression model, the impact of these variables ‎was analyzed on the land use changes in the area. According to the results, the Khamir and ‎Qeshm mangrove forests demonstrate a decreasing trend in 2023 compared to 1989. The results ‎of the prediction of land use changes also revealed that tidal areas and bare lands will increase in ‎‎2060, while mangrove forests and water areas will decrease. Also, the results of the regression ‎model analysis showed that the main descriptive variables affecting land use changes include the ‎distance from settlements and roads due to greater accessibility and the possibility of high ‎development of human activities in these natural habitats. Likewise, controlling the declining ‎trend of Khamir and Qeshm mangrove forests in the future years requires proper planning and ‎integrated management in the correct utilization of these valuable biological reserves.‎

Keywords

Iranian Journal of Remote Sensing & GIS

Articles in Press, Accepted Manuscript
Available Online from 08 May 2024

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