Prediction of the dynamics of land use land cover using a hybrid spatiotemporal model in Iran.

Abstract

Human activities are prone to be the main drivers of land use land cover (LULC) changes, which have cascading effects on the environment and ecosystem services. The main objective of this study is to assess the historical spatiotemporal distributions of LULC changes as well as estimated future scenarios for 2035 and 2045 by considering the explanatory variables of LULC changes in Zanjan province, Iran. The LULC time-series technique was applied using three Landsat images for the years 1987, 2002, and 2019. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) is applied to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization in a hybrid simulation model. Validation of the model's outcome was performed using the Figure of Merit index. The residential area in 1987 was 6406.02ha which increased to 22,857.48ha in 2019 with an average growth rate of 3.97%. Agriculture increased annually by 1.24% and expanded to 149% (890,433ha) of the area occupied in 1987. Rangeland showed a decline concerning its area, with only about 77% (1,502,201ha) of its area in 1987 (1,166,767ha) remaining in 2019. Between 1987 and 2019, the significant net change was a conversion from rangeland to agricultural areas (298,511ha). Water bodies were 8ha in 1987, which increased to 1363ha in 2019, with an annual growth rate of 15.9%. The projected LULC map shows the rangeland will further degrade from 52.43% in 2019 to 48.75% in 2045, while agricultural land and residential areas would be expanded to 940,754ha and 34,727ha in 2045 from 890,434ha and 22,887ha in 2019. The findings of this study provide useful information for the development of an effective plan for the study area.