王兆林, 牙升业, 蒲海霞, Mofakkarul Islam, 欧玲. 基于改进CA-Markov模型的山地城市边缘区土地利用变化模拟(英文)[J]. 农业工程学报, 2020, 36(16): 239-248. DOI: 10.11975/j.issn.1002-6819.2020.16.029
    引用本文: 王兆林, 牙升业, 蒲海霞, Mofakkarul Islam, 欧玲. 基于改进CA-Markov模型的山地城市边缘区土地利用变化模拟(英文)[J]. 农业工程学报, 2020, 36(16): 239-248. DOI: 10.11975/j.issn.1002-6819.2020.16.029
    Wang Zhaolin, Ya Shengye, Pu Haixia, Mofakkarul Islam, Ou Ling. Simulation of spatiotemporal variation of land use in mountainous-urban fringes based on improved CA-Markov model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(16): 239-248. DOI: 10.11975/j.issn.1002-6819.2020.16.029
    Citation: Wang Zhaolin, Ya Shengye, Pu Haixia, Mofakkarul Islam, Ou Ling. Simulation of spatiotemporal variation of land use in mountainous-urban fringes based on improved CA-Markov model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(16): 239-248. DOI: 10.11975/j.issn.1002-6819.2020.16.029

    基于改进CA-Markov模型的山地城市边缘区土地利用变化模拟(英文)

    Simulation of spatiotemporal variation of land use in mountainous-urban fringes based on improved CA-Markov model

    • 摘要: 山地都市边缘区土地利用处于深刻转型中,其变化模拟一直以来都是难点问题。现有的模拟方法如传统的CA-Markov模型等存在明显缺陷。该研究基于层次分析的多标准评价模型MCE引入传统的CA-Markov模型,改进传统模拟方法。在限制因素和限制因子适宜性评价基础上,结合实证区域2006,2011和2016年土地利用现状数据,引入改进的CA-Markov模型,模拟山地城市边缘区土地利用时空变化。首先是利用改进的模型与传统的模拟分别模拟2016年研究区域的土地利用变化;其次将2种方法获取的图像分别与实证区域的土地利用现状图进行精度分析,改进方法模拟精度明显高于传统方法精度;最后利用改进的方法和2016年现状数据,进一步模拟2030年实证区域的土地利用时空格局。结果表明:维持研究区现有的城镇化发展速度,到2030年研究区的耕地、林地、园地、水域、未利用地和农村居民点等将大幅减少,这对于研究区粮食安全、生态保护等造成较大威胁。改进的MCE-adjusted CA-Markov模型,相对于传统的CA-Markov模型,能较好地模拟山地城市边缘区土地利用时空格局变化。此外,讨论了研究结果对该区域国土空间规划编制的启示以及模型未来改进的方向等。

       

      Abstract: Abstract: The simulation of land use change in mountainous urban fringes is always a difficult problem. Existing methods, based on the traditional cellular automata (CA)-Markov coupling model, to capture and simulate such changes in this special areas suffer from notable deficiencies. In this paper, we explore, based on a case study of a mountainous urban fringe in Southwestern China, the performance of an improved method that combines the traditional CA-Markov model with multi-criteria evaluation (MCE). We develop an MCE based AHP model fitted by constraining conditions and factors in the study area. The MCE output is then used as the input of the CA-Markov model to simulate land use changes in the study area based on three periods (2006, 2011, and 2016). Meanwhile, the simulated results by the traditional CA-Markov model are also obtained. Both land use simulation results (the simulation images) are then compared against the observed land use in 2016 (the existing image in 2016) to examine the performance of the improved CA-Markov method. The results show that: 1) Establishing the missing link between the MCE process and the conventional CA-Markov model using land use suitability maps can significantly improve the performance of conventional CA-Markov model in mountainous urban fringes. The results from the case study show that the overall 93.15% simulation accuracy is much higher than that of the average 80% accuracy from most traditional CA-Markov models used in other similar areas. 2) The case study further indicates that from 2006 to 2016, the areas of cultivated land, garden, woodland, grassland, water body, and unused land in study area are decreasing annually. By contrast, the areas of urban land and rural settlement are increasing. The types with the most changes are cultivated land and urban land. This situation indicates that the rapid urbanization in mountainous urban fringe is still a mode of denotation expansion, in which a large amount of agricultural land, especially cultivated land, is occupied by urban construction. 3) The simulation shows that if the current rapid urbanization rate is maintained, then by 2030, the areas of cultivated land , garden , woodland , water body , unused land and rural settlement will be decreased by 6442.46, 923.53, 3239.17, 72.55, 41.26, 352.07 hm2, respectively. The increased land types mainly include urban land and grassland. We also argue the implications of the findings and suggest areas of further work.

       

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