Dynamic simulation of land use based on the LSTM-CA model

LIU Chunlin; XIA Jianxin

doi:10.6046/zrzyyg.2021375

2022 Vol. 34, No. 4

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2022 > 34(4): 122-128

Next Article Previous Article

LIU Chunlin, XIA Jianxin. 2022. Dynamic simulation of land use based on the LSTM-CA model. Remote Sensing for Natural Resources, 34(4): 122-128. doi: 10.6046/zrzyyg.2021375

Citation:

LIU Chunlin, XIA Jianxin. 2022. Dynamic simulation of land use based on the LSTM-CA model. Remote Sensing for Natural Resources, 34(4): 122-128. doi: 10.6046/zrzyyg.2021375

Dynamic simulation of land use based on the LSTM-CA model

College of Life and Environmental Science, Minzu University of China, Beijing 100081, China

Received Date: 05 November 2021

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

Abstract

Timely and accurate acquisition of the spatial pattern evolution of land use can effectively support urban ecological environment protection and scientific management. In this study, the spatial characteristics of land use in multiple periods were extracted using a convolutional neural network. Then, they were combined with multiple spatial driving factors to build the long short-term memory network - cellular automata (LSTM-CA) model. Based on the data of land use classification, terrain, and urban traffic of the Zhangjiakou central urban area in 1995, 2000, 2005, 2010, and 2015, this study investigated the simulation methods for urban land use in 2020. By comparison with the precision of the multi-layer perceptron - cellular automata (MLP-CA) model, the proposed method has a Kappa coefficient of over 0.90 and FoM of 0.39. All indices are better than those of the MLP-CA model. Therefore, the LSTM-CA model can fully explore the internal relationships between the changes in historical land use and effectively improve the simulation precision.
- land use /
- long short-term memory /
- cellular automata /
- change simulation

FullText(HTML)

References

[1]	廖江福, 唐立娜, 王翠平, 等. 城市元胞自动机扩展邻域效应的测量与校准研究[J]. 地理科学进展, 2014, 33(12):1624-1633. Google Scholar
[2]	Liao J F, Tang L N, Wang C P, et al. Measuring and calibrating extended neighborhood effect of urban cellular automata model based on particle swam optimization[J]. Progress in Geography, 2014, 33(12):1624-1633. Google Scholar
[3]	陈宝芬, 张耀民, 江东. 基于CA-ABM模型的福州城市用地扩张研究[J]. 地理科学进展, 2017, 36(5):626-634. Google Scholar
[4]	Chen B F, Zhang Y M, Jiang D. Urban land expansion in Fuzhou City based on coupled cellular automata and agent-based models (CA-ABM)[J]. Progress in Geography, 2017, 36(5):626-634. Google Scholar
[5]	孙毅中, 杨静, 宋书颖, 等. 多层次矢量元胞自动机建模及土地利用变化模拟[J]. 地理学报, 2020, 75(10):2164-2179. Google Scholar
[6]	Sun Y Z, Yang J, Song S Y, et al. Modeling of multi-level vector cellular automata and its simulation of land use change based on urban planning[J]. Acta Geographica Sinica, 2020, 75(10):2164-2179. Google Scholar
[7]	田洁玫, 陈杰. 高标准粮田区鹤壁市土地利用情景模拟预测研究[J]. 国土资源遥感, 2018, 30(1):150-156.doi:10.6046/gtzyyg.2018.01.21. Google Scholar
[8]	Tian J M, Chen J. Simulation and prediction of land use in the high standrad grain area of Hebi City[J]. Remote Sensing for Land and Resources, 2018, 30(1):150-156.doi:10.6046/gtzyyg.2018.01.21. Google Scholar
[9]	杨俊, 解鹏, 席建超, 等. 基于元胞自动机模型的土地利用变化模拟——以大连经济技术开发区为例[J]. 地理学报, 2015, 70(3):461-475. Google Scholar
[10]	Yang J, Xie P, Xi J C, et al. LUCC simulation based on the cellular automata simulation:A case study of Dalian Economic and Technological Development Zone[J]. Acta Geographica Sinica, 2015, 70(3):461-475. Google Scholar
[11]	张大川, 刘小平, 姚尧, 等. 基于随机森林CA的东莞市多类土地利用变化模拟[J]. 地理与地理信息科学, 2016, 32(5):29-36. Google Scholar
[12]	Zhang D C, Liu X P, Yao Y, et al. Simulating spatiotemporal change of multiple land use types in Dongguan by using random forest based on cellular automata[J]. Geography and Geo-Information Science, 2016, 32(5):29-36. Google Scholar
[13]	Xing W, Qian Y, Guan X, et al. A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation[J]. Computers and Geosciences, 2020, 137(1):1-9. Google Scholar
[14]	Von Neumann J. Theory of self-reproducing automata[J]. London:University of Illinois Press, 1966:3-14. Google Scholar
[15]	Karimi F, Sultana S, Babakan A S, et al. An enhanced support vector machine model for urban expansion prediction[J]. Computers,Environment and Urban Systems, 2019, 75(1):61-75. Google Scholar
[16]	Shafizadeh-Moghadam H, Asghari A, Tayyebi A, et al. Coupling machine learning,tree-based and statistical models with cellular automata to simulate urban growth[J]. Computers,Environment and Urban Systems, 2017, 64(1):297-308. Google Scholar
[17]	He J, Li X, Yao Y, et al. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques[J]. International Journal of Geographical Information Science, 2018, 32(10):2076-2097. Google Scholar
[18]	Gounaridis D, Chorianopoulos I, Symeonakis E, et al. A random forest-cellular automata modelling approach to explore future land use/cover change in Attica (Greece),under different socio-economic realities and scales[J]. Science of the Total Environment, 2019, 64(6):320-335. Google Scholar
[19]	Grekousis G. Artificial neural networks and deep learning in urban geography:A systematic review and meta-analysis[J]. Computers,Environment and Urban Systems, 2019, 74(1):244-256. Google Scholar
[20]	Guan D, Zhao Z, Tan J. Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models:A case study in three gorges reservoir area of Chongqing,China[J]. Environmental Science and Pollution Research, 2019, 26(20):20669-20688. Google Scholar
[21]	Jia X, Khandelwal A, Nayak G, et al. Incremental dual-memory lstm in land cover prediction[C]// Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017:867-876. Google Scholar
[22]	Wang H, Zhao X, Zhang X, et al. Long time series land cover classification in China from 1982 to 2015 based on Bi-LSTM deep learning[J]. Remote Sensing, 2019, 11(14):1-22. Google Scholar
[23]	White R, Engelen G. Cellular automata and fractal urban form:A cellular modelling approach to the evolution of urban land-use patterns[J]. Environment and Planning A, 1993, 25(8):1175-1199. Google Scholar
[24]	Liu X, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017, 168(1):94-116. Google Scholar