2025 Vol. 34, No. 2
Article Contents

JIANG Shan, DENG Ying, SHI Shao-shan, YAO Yuan, WEI Ming-hui, LIU Kai, FANG Na-na, ZHOU Li. Spatiotemporal evolution pattern and driving force of land use in resource-based cities: A case study of Anshan City, Liaoning Province[J]. Geology and Resources, 2025, 34(2): 232-245. doi: 10.13686/j.cnki.dzyzy.2025.02.011
Citation: JIANG Shan, DENG Ying, SHI Shao-shan, YAO Yuan, WEI Ming-hui, LIU Kai, FANG Na-na, ZHOU Li. Spatiotemporal evolution pattern and driving force of land use in resource-based cities: A case study of Anshan City, Liaoning Province[J]. Geology and Resources, 2025, 34(2): 232-245. doi: 10.13686/j.cnki.dzyzy.2025.02.011

Spatiotemporal evolution pattern and driving force of land use in resource-based cities: A case study of Anshan City, Liaoning Province

More Information
  • Based on land use/cover data for the years of 2000, 2010, and 2020, this study analyzes the spatiotemporal evolution characteristics and driving forces of land use in Anshan City during 2000-2020 and predicts the land use pattern in 2040 by ways of land use dynamic degree, geo-information maps, chord diagram visualization and PLUS model. The results show that the area of cultivated land continuously decreased, with a total reduction of 496.10 km2 during 2000-2020, primarily converted to forest, construction and grass lands. Forest land increased 410.86 km2 during the same period, mainly transferred from cultivated and grass lands. The most significant land use change in recent 20 years behaves as cultivated land → forest land and cultivated land → construction land, with the former predominantly distributed in the southeastern Haicheng City and Qianshan District, and the latter concentrated in Qianshan District, central Haicheng City, and central Tai'an County. Since 2000, the land use change rate has showed a fast-slow temporal pattern, with a comprehensive land use dynamic degree of 1.29% in the first decade and 2.84% in the second decade. In terms of single land type, construction land shows the fastest growth rate at 0.87% annually, while cultivated land experiences the most rapid decline at 0.60% per year over the 20-year span. The simulation prediction results indicate further reduction of cultivated land in Anshan City by 2040 due to the main driving factors of elevation, precipitation and temperature, followed by socioeconomic factors including population, GDP, and distances from railways and local administrative center.

  • 加载中
  • [1] Foley J A, DeFries R, Asner G P, et al. Global consequences of land use[J]. Science, 2005, 309(5734): 570-574. doi: 10.1126/science.1111772

    CrossRef Google Scholar

    [2] IGBP. Global land project: Science plan and implementation strategy [R]. Stockholm: IGBP, 2005.

    Google Scholar

    [3] Turner Ⅱ B L, Lambin E F, Reenberg A. The emergence of land change science for global environmental change and sustainability[J]. Proceedings of the National Academy of Sciences, 2007, 104(52): 20666-20671. doi: 10.1073/pnas.0704119104

    CrossRef Google Scholar

    [4] Lambin E F, Baulies X, Bockstael N et al. Land-use and land-cover change (LUCC): Implementation strategy[C]//A core project of the International Geospere-Biosphere Programme and the International Human Dimensions Programme on Global Environmental Change. Stockholm: IGBP, 1995: 125.

    Google Scholar

    [5] 刘纪远, 刘明亮, 庄大方, 等. 中国近期土地利用变化的空间格局分析[J]. 中国科学(D辑), 2002, 32(12): 1031-1040.

    Google Scholar

    Liu J Y, Liu M L, Zhuang D F, et al. Analysis of time-space patterns of recent landuse variations in China[J]. Sciences in China (D Series), 2002, 32(12): 1031-1040.

    Google Scholar

    [6] Brandt J, Primdahl J, Reenberg A. Rural land-use and landscape dynamics: analysis of driving forces in space and time[C]//Land-use changes and their environmental impact in rural areas in Europe. 1999: 81-102.

    Google Scholar

    [7] López E, Bocco G, Mendoza M, et al. Predicting land-cover and land-use change in the urban fringe: A case in Morelia City, Mexico[J]. Landscape and Urban Planning, 2001, 55(4): 271-285 doi: 10.1016/S0169-2046(01)00160-8

    CrossRef Google Scholar

    [8] Gao C J, Zhou P, Jia P, et al. Spatial driving forces of dominant land use/land cover transformations in the Dongjiang River watershed, Southern China[J]. Environmental Monitoring and Assessment, 2016, 188(2): 84. doi: 10.1007/s10661-015-5088-z

    CrossRef Google Scholar

    [9] 史培军, 陈晋, 潘耀忠. 深圳市土地利用变化机制分析[J]. 地理学报, 2000, 55(2): 151-160. doi: 10.3321/j.issn:0375-5444.2000.02.003

    CrossRef Google Scholar

    Shi P J, Chen J, Pan Y Z. Landuse change mechanism in Shenzhen City[J]. Acta Geographica Sinica, 2000, 55(2): 151-160. doi: 10.3321/j.issn:0375-5444.2000.02.003

    CrossRef Google Scholar

    [10] 刘纪远, 张增祥, 徐新良, 等. 21世纪初中国土地利用变化的空间格局与驱动力分析[J]. 地理学报, 2009, 64(12): 1411-1420.

    Google Scholar

    Liu J Y, Zhang Z X, Xu X L, et al. Spatial patterns and driving forces of land use change in China in the early 21st Century[J]. Acta Geographica Sinica, 2009, 64(12): 1411-1420.

    Google Scholar

    [11] Wang J, Chen Y Q, Shao X M, et al. Land-use changes and policy dimension driving forces in China: Present, trend and future[J]. Land Use Policy, 2012, 29(4): 737-749. doi: 10.1016/j.landusepol.2011.11.010

    CrossRef Google Scholar

    [12] Du X D, Jin X B, Yang X L, et al. Spatial pattern of land use change and its driving force in Jiangsu Province[J]. International Journal of Environmental Research and Public Health, 2014, 11(3): 3215-3232. doi: 10.3390/ijerph110303215

    CrossRef Google Scholar

    [13] 杨佳佳, 吕骏超, 金珊合, 等. 全球黑土区耕地动态变化及其驱动力Logistic回归分析[J]. 地质与资源, 2023, 32(5): 584-591. doi: 10.13686/j.cnki.dzyzy.2023.05.008

    CrossRef Google Scholar

    Yang J J, Lyu J C, Jin S H, et al. Farmland dynamic change and its driving force analysis in global black soil regions based on logistic regression model[J]. Geology and Resources, 2023, 32(5): 584-591. doi: 10.13686/j.cnki.dzyzy.2023.05.008

    CrossRef Google Scholar

    [14] 王璐晨, 韩海辉, 张俊, 等. 塔里木河流域土地利用及人类活动强度的时空演化特征研究[J]. 中国地质, 2024, 51(1): 203-220.

    Google Scholar

    Wang L C, Han H H, Zhang J, et al. Spatio-temporal evolution of land use and human activity intensity in the Tarim River Basin, Xinjiang[J]. Geology in China, 2024, 51(1): 203-220.

    Google Scholar

    [15] 王鹏, 赵君, 阎晓娟, 等. 动态时空视角下黄河流域城市土地利用效率的集聚演化特征[J]. 中国地质, 2023, 50(2): 506-520.

    Google Scholar

    Wang P, Zhao J, Yan X J, et al. Agglomeration and evolution characteristics of urban land-use efficiency under a dynamic spatio-temporal perspective in the Yellow River Basin[J]. Geology in China, 2023, 50(2): 506-520.

    Google Scholar

    [16] 黄钰清, 李骁尧, 于强, 等. 1995—2018年黄河流域土地利用变化及驱动力分析[J]. 西北林学院学报, 2022, 37(6): 113-121.

    Google Scholar

    Huang Y Q, Li X Y, Yu Q, et al. An analysis of land use change and driving forces in the Yellow River Basin from 1995 to 2018[J]. Journal of Northwest Forestry University, 2022, 37(6): 113-121.

    Google Scholar

    [17] 江山, 石旭飞, 郭常来, 等. 基于CA-Markov模型的大凌河流域土地利用变化与模拟预测研究[J]. 地质与资源, 2023, 32(5): 624-632. doi: 10.13686/j.cnki.dzyzy.2023.05.013

    CrossRef Google Scholar

    Jiang S, Shi X F, Guo C L, et al. Land use changes and simulation prediction in Daling River Basin based on CA-Markov model[J]. Geology and Resources, 2023, 32(5): 624-632. doi: 10.13686/j.cnki.dzyzy.2023.05.013

    CrossRef Google Scholar

    [18] 朱文博, 张静静, 崔耀平, 等. 基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 2019, 74 (3): 446-459.

    Google Scholar

    Zhu W B, Zhang J J, Cui Y P, et al. Assessment of territorial ecosystem carbon storage based on land use change scenario: A case study in Qihe River Basin[J]. Acta Geographica Sinica, 2019, 74 (3): 446-459.

    Google Scholar

    [19] 朱康文, 李月臣, 周梦甜. 基于CLUE-S模型的重庆市主城区土地利用情景模拟[J]. 长江流域资源与环境, 2015, 24(5): 789-797.

    Google Scholar

    Zhu K W, Li Y C, Zhou M T. Land use scenario simulation of the main city of Chongqing based on the CLUE-S model[J]. Resources and Environment in the Yangtze Basin, 2015, 24(5): 789-797.

    Google Scholar

    [20] 刘晓娟, 黎夏, 梁迅, 等. 基于FLUS-InVEST模型的中国未来土地利用变化及其对碳储量影响的模拟[J]. 热带地理, 2019, 39 (3): 397-409.

    Google Scholar

    Liu X J, Li X, Liang X, et al. Simulating the change of terrestrial carbon storage in China based on the FLUS-InVEST model[J]. Tropical Geography, 2019, 39(3): 397-409.

    Google Scholar

    [21] 高禄年, 董会和. 城市收缩背景下的资源型城市发展与效率评价: 以鞍山市为例[J]. 中国矿业, 2022, 31(3): 42-48.

    Google Scholar

    Gao L N, Dong H H. Development and efficiency evaluation of resource-based cities under the background of urban shrinkage: A case study of Anshan City[J]. China Mining Magazine, 2022, 31 (3): 42-48.

    Google Scholar

    [22] 王秀兰, 包玉海. 土地利用动态变化研究方法探讨[J]. 地理科学进展, 1999, 18(1): 81-87.

    Google Scholar

    Wang X L, Bao Y H. Study on the methods of land use dynamic change research[J]. Progress in Geography, 1999, 18(1): 81-87.

    Google Scholar

    [23] Liang X, Guan Q F, Clarke K C, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China[J]. Computers, Environment and Urban Systems, 2021, 85: 101569.

    Google Scholar

    [24] 李琛, 高彬嫔, 吴映梅, 等. 基于PLUS模型的山区城镇景观生态风险动态模拟[J]. 浙江农林大学学报, 2022, 39(1): 84-94.

    Google Scholar

    Li C, Gao B P, Wu Y M, et al. Dynamic simulation of landscape ecological risk in mountain towns based on PLUS model[J]. Journal of Zhejiang A&F University, 2022, 39(1): 84-94.

    Google Scholar

    [25] 曲衍波, 王世磊, 朱伟亚, 等. 黄河三角洲国土空间演变的时空分异特征与驱动力分析[J]. 农业工程学报, 2021, 37(6): 252-263.

    Google Scholar

    Qu Y B, Wang S L, Zhu W Y, et al. Spatial-temporal differentiation characteristics and driving force of territorial space evolution in the Yellow River Delta[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(6): 252-263.

    Google Scholar

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(10)

Tables(7)

Article Metrics

Article views(182) PDF downloads(55) Cited by(0)

Access History

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint