Spatiotemporal Prediction of Future LULC changes, Northern and Northeastern Parts of Iraq with MOLUSCE

Abstract

The objective of this study was to develop a model for predicting future alterations in land use as well as land cover (LULC) within the low folded zone of Iraq by utilizing remote sensing data spanning for the period from 2000 to 2020. An integrated Cellular Automata-Artificial Neural Network (CA-ANN) methodology which is provided by the Molusce Plugin model in QGis was employed to produce Land Use and Land Cover (LULC) maps for the years 2020, 2040, 2060, and 2100. The results of the validation K (overal)= 0.83499, K(location)= 0.8586, K(histo) = 0.97245, % of correctness =93.044 and R²= 0.9997 demonstrated a high level of concordance between the classed maps and the maps generated by the model. Future predictions demonstrate that the built-up land will increase (from 2481.95 to 16347.77 km²), barren land, water bodies, (Dense, Sparse) vegetation, Plantation, and Agricultural fallow and Agricultural land will decrease (from 22595.4 to 19129.18 km²), (from 672.1 to 562.29 km²), (from 1059.83 to 425.73 km²), (from 962.50 to 320.20 km²), (from 1196.75 to 428.5 km²), (from 15172.0389 to 10644.1551 km²) and (from15877.071 to 12408.32 km²) respectively. The decrease in the future agricultural land will impact the water and food security of this zone. These forecasts can assist in recognizing probable ecological consequences, such as alterations in water availability, agricultural stability, and the depletion of natural habitats. In general, MOLUSCE is a beneficial instrument for forecasting and evaluating forthcoming changes in land use and land cover (LULC), as well as aiding in the promotion of sustainable land use planning endeavors. The implications of the findings are significant for a range of stakeholders, such as urban planners, policymakers, environmental scientists, conservation biologists, non-governmental organizations (NGOs), and water resources managers.