Land Use/Land Cover Change Modeling and Prediction Using Artificial Neural Networks: A Case Study of the Birjand Watershed

Human development and land transformation have turned land use and land cover (LULC) change into one of the major global environmental challenges, affecting essential biogeochemical cycles such as water, carbon, and energy. This study aims to model and predict LULC changes in the Birjand watershed, located in eastern Iran, using an Artificial Neural Network (ANN) within the Land Change Modeler (LCM) framework. Landsat satellite imagery (TM and ETM+) was used to detect historical changes, while eight explanatory variables digital elevation model, slope, soil texture, and distance from roads, rivers, faults, residential areas, and agricultural lands were applied to simulate transition potentials for the ۲۰۰۰–۲۰۱۰ calibration period. A Markov chain analysis was then employed to estimate the probability of transitions between land use classes, and predicted maps were generated for ۲۰۲۰. The results revealed that barren lands were predominantly converted into residential areas, reflecting rapid urban expansion. Validation of the simulated ۲۰۲۰ map against observed data indicated strong model performance, supported by accuracy metrics such as the Kappa coefficient and Overall Accuracy. Based on the validated model, LULC projections for ۲۰۴۰ show a continued decline in agricultural lands alongside the expansion of urban and barren areas. The projected areas amount to approximately ۷۳.۸۲ km² of agricultural land, ۶۸.۸۱ km² of urban land, and ۳,۲۸۲.۳۹ km² of barren land. These projections underscore the urgent need for sustainable land-use planning and management strategies to minimize environmental degradation in arid regions.

کلیدواژه ها:

Artificial Neural Network (ANN) ، prediction ، Markov Vhain ، Land Change Modeler (LCM) ، Birjand watershed