One of the most dynamic components of the environment is land use land cover (LULC), which have been changing remarkably since after the industrial revolution at various scales. Frequent monitoring and quantifying LULC change dynamics provide a better understanding of the function and health of ecosystems. This study aimed at modelling the future changes of LULC for the Erbil governorate in the Kurdistan region of Iraq (KRI) using the synergy Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e., 1988, 2002, and 2017) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with several class categories were generated, and then change-detection analysis was executed. Using the classified (1988–2002) and (2002–2017) LULC maps in the hybrid model, LULC maps for 2017 and 2050 were modelled respectively. The model output (modelled 2017) was validated with the classified 2017 LULC map. The accuracy of agreements between the classified and the modelled maps were K_no = 0.8339, K_location = 0.8222, K_standard = 0.7491, respectively. Future predictions demonstrate between 2017 and 2050, built-up land, agricultural land, plantation, dense vegetation and water body will increase by 173.7% (from 424.1 to 1160.8 km²), 79.5% (from 230 to 412.9 km²), 70.2% (from 70.2 to 119.5 km²), 48.9% (from 367.2 to 546.9 km²) and 132.7% (from 10.7 to 24.9 km²), respectively. In contrast, sparse vegetation, barren land will decrease by 9.7% (2274.6 to 2052.8 km²), 18.4% (from 9463.9-7721 km²), respectively. The output of this study is invaluable for environmental scientists, conservation biologists, nature-related NGOs, decision-makers, and urban planners.

环境中最具活力的组成部分之一是土地利用土地覆被（LULC），自从工业革命后发生了各种规模以来，土地覆被一直在发生着显着变化。经常监测和量化LULC变化动态可以更好地了解生态系统的功能和健康状况。这项研究旨在使用协同自动元胞（CA）-马尔可夫模型对伊拉克库尔德地区（KRI）的埃尔比勒省的LULC的未来变化进行建模。为此，使用最大似然分类器对连续三年的Landsat影像（即1988、2002和2017）进行了分类。从分类中，生成具有几个类别类别的三个LULC映射，然后执行变化检测分析。在混合模型中使用分类的（1988–2002）和（2002–2017）LULC映射，分别对2017年和2050年的LULC地图建模。模型输出（建模为2017）已通过分类的2017 LULC映射进行了验证。分类地图和建模地图之间协议的准确性为K_no  = 0.8339，K_位置 = 0.8222，K_标准 = 0.7491。未来的预测表明，2017年至2050年之间，已建成土地，农业用地，人工林，茂密的植被和水体将分别增长173.7％（从424.1至1160.8 km ²），79.5％（从230至412.9 km ²），70.2％ （从70.2至119.5 km ²），48.9％（从367.2至546.9 km ²）和132.7％（从10.7至24.9 km ²）。相比之下，植被稀疏的荒地将减少9.7％（从2274.6降低至2052.8 km ²），18.4％（从9463.9-7721 km ^2减少））， 分别。对于环境科学家，保护生物学家，与自然相关的非政府组织，决策者和城市规划者来说，这项研究的成果是无价的。