Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China.
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Int J Environ Res Public Health. 2022 Jul 19;19(14):8785. doi: 10.3390/ijerph19148785.
Until now, few studies have used the mainstreaming models to simulate the land use changes in the cities of rapid urbanizing regions. Therefore, we aimed to develop a methodology to simulate the land use changes in rapid urbanizing regions that could reveal the land use change trend in the cities of the regions. Taking the urban areas of Wuhan, a typical rapid urbanizing region in China, as the study area, this study built a Markov chain-artificial neural network (ANN)-cellular automaton (CA) coupled model. The model used land use classification spatial data with a spatial resolution of 5 m in 2010 and 2020, obtained by remote sensing image interpretation, and data on natural and socio-economic driving forces for land use change simulation. Using the coupled model, the land use patterns of Wuhan urban areas in 2020 were simulated, which were validated in comparison with the actual land use data in 2020. Finally, the model was used to simulate the land uses in the study area in 2030. The model validation indicates that the land use change simulation has a high accuracy of 90.7% and a high kappa coefficient of 0.87. The simulated land uses of the urban areas of Wuhan show that artificial surfaces will continue to expand, with an area increase of approximately 7% from 2020 to 2030. Moreover, the area of urban green spaces will also increase by approximately 7%, while that of water bodies, grassland, cropland, and forests will decrease by 12.6%, 13.6%, 34.9%, and 1.3%, respectively, from 2020 to 2030. This study provides a method of simulating the land use changes in the cities of rapid urbanizing regions and helps to reveal the patterns and driving mechanisms of land use change in Wuhan urban areas.
到目前为止，很少有研究使用主流化模型来模拟快速城市化地区城市的土地利用变化。因此，我们旨在开发一种方法来模拟快速城市化地区的土地利用变化，以揭示该地区城市的土地利用变化趋势。本研究以中国典型快速城市化地区武汉市的城区为研究区，构建了马尔可夫链-人工神经网络（ANN）-元胞自动机（CA）耦合模型。该模型使用了土地利用分类空间数据，空间分辨率为 5 米，数据来源于 2010 年和 2020 年的遥感图像解译，以及土地利用变化的自然和社会经济驱动力数据。利用该耦合模型，模拟了武汉市城区 2020 年的土地利用格局，并与 2020 年的实际土地利用数据进行了验证。最后，利用该模型模拟了研究区 2030 年的土地利用。模型验证表明，土地利用变化模拟的准确率为 90.7%，kappa 系数为 0.87。模拟的武汉市城区土地利用结果表明，人工表面将继续扩张，从 2020 年到 2030 年，面积将增加约 7%。此外，城市绿地面积也将增加约 7%，而水体、草地、耕地和森林的面积将分别减少 12.6%、13.6%、34.9%和 1.3%。本研究提供了一种模拟快速城市化地区城市土地利用变化的方法，有助于揭示武汉市城区土地利用变化的模式和驱动机制。
