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| Evolution and Prediction of Land Use Simulation Model Based on
Serpentine Route Method and Multispectral Technology |
| ZHENG Yu-tao1, 2, YANG Kai-xin3, MAO Hai-ying1*, YU Jing-xin4 |
1. Beijing City University, Beijing 100191, China
2. College of Applied Arts and Sciences, Beijing Union University, Beijing 100083, China
3. Tianjin College, University of Science and Technology Beijing, Tianjin 300000, China
4. Beijing Academy of Agriculture and Forestry Sciences, Beijing 100006, China
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Abstract This study utilizes multi-spectral MS300 data from Changping District, Beijing, monitored at five-year intervals (2010, 2015, 2020). Simultaneously, the serpentine route aerial survey method using unmanned aerial vehicles (UAVs) was employed to supplement data collection in key areas. Data fusion techniques were applied to tightly integrate RGB and multi-spectral data tightly, thereby mitigating the impact of weather and other factors on land classification accuracy in certain regions. The study also introduced methodologies, including the land-use dynamic degree and transfer matrix. Using the FLUS model, a comprehensive simulation and prediction of the land use situation in Changping District for 2035 was conducted. The analysis identified land-use change patterns in Changping from 2010 to 2020, with accuracy verified using the Kappa coefficient. Furthermore, incorporating eight driving factors (such as topography, geomorphology, and transportation) identified through multicollinearity tests performed using Python, Markov chain prediction within the FLUS model was used to forecast the land use changes in Changping District for 2035. The specific results are as follows: (1) In the land-use changes in Changping District from 2010 to 2020, grassland exhibited significant transformation, with a dynamic change rate of 23.88%. This change primarily involved conversion from northern areas to central and eastern regions. Water bodies and woodland showed steady growth, mainly through mutual conversions with cropland. Built-up (construction) land and cultivated land experienced minor reductions, with dynamic change rates of -1.64% and -0.36% respectively, indicating relatively stable changes. Compared with observed land-use outcomes, this study aligns with the “Returning Farmland to Forest and Grassland Program” land-use policy implemented between 2010 and 2020, which emphasized “strictly controlling the conversion of cultivated land to woodland, garden land, and other types of agricultural land”. This alignment also supports the reliability of the ZY-3 (Resource Satellite-3) MS300 multispectral data and the FLUS model. (2) Based on research and analysis of land-use type conversions in the Changping District from 2010 to 2020, this study utilizes the FLUS model to conduct a natural progression prediction of land-use types for 2035. We also simulated three scenarios—green/low-carbon development, cropland protection, and ecological conservation. The results indicate that by 2035, the degradation of grassland in the region will be relatively significant, while changes in other land-use types, such as built-up land, cultivated land, and water bodies, will remain relatively stable. Specifically, the land-use change trend in Changping District under the green and low-carbon scenario from 2020 to 2035 aligns closely with the land-use change patterns observed from 2010 to 2020. This alignment also corresponds with the policy of development oriented towards reduction. These results suggest that over the next decade, the urbanization pace in Beijing's Changping District will be relatively slow, and further economic development will not drastically disrupt fundamental land-use patterns in the short term. However, the degradation of grassland serves as a constant reminder of the importance of environmental protection. The findings of this study can provide a basis for reasonably predicting land-use type conversions in various provinces and cities, laying a theoretical and practical foundation for future urban planning and development.
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Received: 2025-02-10
Accepted: 2025-08-25
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Corresponding Authors:
MAO Hai-ying
E-mail: rubymm@126.com
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[1] LEI Xue, WANG Qian-na, DENG Jing-lin, et al(雷 雪, 王倩娜, 邓靖琳，等). Journal of Sichuan Normal University(Natural Science)[四川师范大学学报(自然科学版)], 2022, 45(6): 806.
[2] ZHAO Lin-feng, LIU Xiao-ping, LIU Peng-hua, et al(赵林峰, 刘小平, 刘鹏华, 等). Journal of Geo-Information Science（地球信息科学学报）, 2020, 22(3): 517.
[3] JIANG Xiao-fang, DUAN Han-chen, LIAO Jie, et al(蒋小芳, 段翰晨, 廖 杰, 等). Arid Zone Research(干旱区研究), 2022, 39(4): 1246.
[4] GUO Hong-wei, SUN Xiao-yin, LIAN Li-shu, et al(郭洪伟, 孙小银, 廉丽姝, 等). Chinese Journal of Applied Ecology(应用生态学报), 2016, 27(9): 2899.
[5] LI Li, HU Rui-ke, LI Su-hong(李 立, 胡睿柯, 李素红). Remote Sensing for Natural Resources(自然资源遥感), 2023, 35(1): 81.
[6] LI Xue, LI Wen, SHI Song, et al(李 雪, 李 文, 石 淞, 等). Bulletin of Soil and Water Conservation(水土保持通报), 2023, 43(3): 320.
[7] PAN Li-hu, QIN Shi-peng, LI Xiao-wen, et al(潘理虎, 秦世鹏, 李晓文, 等). Computer Technology and Development(计算机技术与发展), 2021, 31(5): 186.
[8] XU Chong-min, CHEN Jin, ZHANG Lu-dan, et al(徐崇敏, 陈 瑾, 张露丹, 等). Bulletin of Soil and Water Conservation(水土保持通报), 2022, 42(6): 155.
[9] LONG Yuan, LI Ping, TANG Yue-liang, et al(龙 远, 李 萍, 唐月亮, 等). Acta Agriculturae Jiangxi(江西农业学报), 2020, 32(5): 126.
[10] LI Yuan-jie, YE Chang-sheng, HUANG Xiao-lan(李媛洁, 叶长盛, 黄小兰). Research of Soil and Water Conservation(水土保持研究), 2021, 28(5): 325.
[11] HU Feng, ZHANG Yan, GUO Yu, et al(胡 丰, 张 艳, 郭 宇, 等). Arid Land Geography(干旱区地理), 2022, 45(4): 1125.
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