Investigating the spatiotemporal evolution characteristics of forests and identifying the contributions of driving factors using GIS technology and machine learning models: A case study of Yibin City, China

Wanxin Huang, Yuanjie Deng, Hang Chen, Yifeng Hai, Aiting Ma, Meixuan Duan

Article ID: 11558
Vol 8, Issue 1, 2025


Abstract


This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.


Keywords


forests; spatiotemporal evolution; driving factors; Yibin City

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References


1. Hua F, Bruijnzeel LA, Meli P, et al. The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. Science. 2022; 376(6595): 839–844. doi: 10.1126/science.abl4649

2. Weiskopf SR, Isbell F, Arce-Plata MI, et al. Biodiversity loss reduces global terrestrial carbon storage. Nature Communications. 2024; 15(1): 4354. doi: 10.1038/s41467-024-47872-7

3. Zhou H, Hu L, Yu S. Research on Spatial Change of Forestland in Nanning City Based on GIS Platform and Spatial Autocorrelation Model. Guangdong Agricultural Sciences. 2022; 49(1): 51–61. doi: 10.16768/j.issn.1004-874X.2022.01.006

4. Wei W, Dong N. Study on Landscape Planning and Design of Shanghai Typical Rural Areas Under the Target of Low carbon Rural Area (Chinese). Landscape Architecture. 2022; 39(1): 33–40. doi: 10.12193/j.laing.2022.01.0033.005

5. Tang C, Huang T, Wang J. Predictive Analysis and Conservation Management Recommendations for Forest Land Acquisition and Occupation in Qingtian County Based on Grey Theory (Chinese). Green Science and Technology. 2023; 25(17): 131–136.

6. Cui L, Wang L, Zhang Q, et al. Spatio-temporal Variation Characteristics and Influencing Factors of Protection Forest Area Change in the Five Phase of Three-North Shelterbelt Program Region of Heilongjiang (Chinese). Forestry Science & Technology. 2024; 49(3): 54–57. doi: 10.19750/j.cnki.1001-9499.2024.03.011

7. Deng Y, Hou M, Zhang X, et al. Drivers of forestland change in the Qinba Mountain region of Shaanxi based on the Logistic regression model. Journal of Nanjing Forestry University (Natural Sciences Edition). 2022; 46(1): 106–114.

8. Chen B, Yang D, Xu S, et al. Analysis of spatial and temporal changes and attribution discrimination of forestland in Hubei Province from 1990 to 2015 (Chinese). Ecological Science. 2021; 40(6): 125–132. doi: 10.14108/j.cnki.1008-8873.2021.06.015

9. Xiang S. Research on spatio temporal change of land use/cover and its driving forces in Cili Country [PhD thesis]. Jishou University; 2017.

10. Zhang Q. Spatia-Temporal Change and its Human Driving Forces of woodlands in the Qinling Mountain during the last 30 years [PhD thesis]. Northwest University; 2010.

11. Kan Z, Yun S, Minjie N. Research on the temporal and spatial changes of vegetation coverage in Qinling mountains based on Google Earth Engine platform (Chinese). Bulletin of Surveying and Mapping. 2022; 5: 49. doi: 10.13474/j.cnki.11-2246.2022.0140

12. Liu X, Liu H. The application of ANN-FLUS model in reconstructing historical cropland distribution changes: A case study of Vietnam from 1885 to 2000. Journal of Natural Resources. 2024; 39(6): 1473–1492.

13. Guo PC. Research on Land Use/Cover Structure and Space Optimization by Coupling MOP and PLUS Models——A Case Study of Hefei City [PhD thesis]. Hefei University of Technology; 2021.

14. Yuan X. Scenario simulation of land use change and landscape ecological risk research in Wuhan based on PLUS model [PhD thesis]. East China University of Technology; 2022.

15. Sun X, Xue J, Dong L. Spatiotemporal Change and Prediction of Carbon Storage in Nanjing Ecosystem Based on PLUS Model and in VEST Model. Journal of Ecology and Rural Environment. 2023; 39(1): 41–51. doi: 10.19741/j.issn.1673-4831.2022.0953

16. Fan Y. Endless vitality, pursuing “green” along the way: How Yibin builds a pioneer zone for ecological priority and green low-carbon development. Yibin Daily. 2024.

17. Liu M. Impacts of climate change and human activities on the trade-off and synergy for Ecosystem Service in China from 1992 to 2015 [PhD thesis]. Guizhou Normal University; 2022.

18. Deng J, Wang K, Hong Y, et al. Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Planning. 2009; 92(3): 187–198. doi: 10.1016/j.landurbplan.2009.05.001

19. Huang B, Huang J, Gilmore Pontius R, et al. Comparison of Intensity Analysis and the land use dynamic degrees to measure land changes outside versus inside the coastal zone of Longhai, China. Ecological Indicators. 2018; 89: 336–347. doi: 10.1016/j.ecolind.2017.12.057

20. Xie M, Zhou H, Chen S, et al. Spatiotemporal Evolution of Land Use and Its Driving Forces in Lüliang City of Shanxi Province. Bulletin of Soil and Water Conservation. 2024; 44(3): 296–306. doi: 10.13961/j.cnki.stbctb.2024.03.030

21. Luo F, Pan A, Chen ZS, et al. Spatiotemporal Pattern Change of Cultivated Land and Its Driving Forces in Yibin City, Sichuan Province During 1980–2018 (Chinese). Bulletin of Soil and Water Conservation. 2021; 41(6): 336–344. doi: 10.13961/j.cnki.stbctb.20210917.001

22. Wang R, Zhao J, Lin Y et al. Land change simulation and forest carbon storage of central Yunnan urban agglomeration, China based on SSP-RCP scenarios. Forests. 2022; 13(12): 2030. doi: 10.3390/f13122030

23. Hiltner U, Bräuning A, Gebrekirstos A, et al. Impacts of precipitation variability on the dynamics of a dry tropical montane forest. Ecological Modelling. 2016; 320: 92–101. doi: 10.1016/j.ecolmodel.2015.09.021

24. Koehn CR, Petrie MD, Bradford JB, et al. Seasonal Precipitation and Soil Moisture Relationships Across Forests and Woodlands in the Southwestern United States. Journal of Geophysical Research: Biogeosciences. 2021; 126(4): e2020JG005986. doi: 10.1029/2020JG005986

25. Jin C, Yabuta M. Economic analysis of China’s grain for green policy: theory and evidence. Asia-Pacific Journal of Regional Science. 2024; 8(1): 355–376. doi: 10.1007/s41685-024-00331-z

26. Lu L, Marcos-Martinez R, Xu Y, et al. The spatiotemporal patterns and pathways of forest transition in China. Land Degradation & Development. 2021; 32(18): 5378–5392. doi: 10.1002/ldr.4115

27. Laurance W. As roads spread in rainforests, the environmental toll grows. Yale Environment 360 Magazine. 2012; 1–6.

28. Forzieri G, Dakos V, Mcdowell NG, et al. Emerging signals of declining forest resilience under climate change. Nature. 2022; 608(7923): 534–539. doi: 10.1038/s41586-022-04959-9

29. Zhang Q, Wang P, Chen H, et al. Spatial-temporal Change of Land-use Pattern in Upper Region of Yangtze River——A Case Study in Yibin City of Sichuan Province. Bulletin of Soil and Water Conservation. 2018; 38(2): 210–216. doi: 10.13961/j.cnki.stbctb.2018.02.034

30. Luo F. Temporal and spatial changes of ecosystem service value in the hilly region of southern Sichuan: A case study of Yibin City [PhD thesis]. China West Normal University; 2022.

31. Liu M, Xia X, Chen Q, et al. Investigation on the current status of “non-agricultural” and “non- grain” utilization of cultivated land resources in Hehuang Valley (Chinese). Research of Agricultural Modernization. 2025; 1–12. doi: 10.13872/j.1000-0275.2024.1268

32. Ding Y, Zhang Y, Wu P, et al. Study on Driving Factors of Cultivated Land Intensive Utilization in China (Chinese). Henan Science. 2024; 42(11): 1681–1690.

33. Liu J. Study on the Coordination between the Expansion of Construction Land and Comprehensive Economic Development in Sichuan Province based on Decoupling Model [PhD thesis]. Sichuan Normal University; 2017.

34. Xie Y, Wang Q, Luo Y. City-level Urban Green Infrastructure Evaluation Index System Based on MSPA——A Case Study of Major Cities in Sichuan Province (Chinese. Chinese Landscape Architecture. 2020; 36(7): 87–92. doi: 10.19775/j.cla.2020.07.0087

35. Feng Y, Zhou B, Liao Y. Study on Land Use Change and Its Eco-Environmental Effects in Yibin City . Journal of Sichuan Forestry Science and Technology. 2023; 44(2): 122–129.

36. Dang Y. Land use change in Tao River and driving force analysis [PhD thesis]. Beijing Forestry University; 2020.

37. Zhao J. Research on land use change and simulation for ecological security in the Hengduan Mountains [PhD thesis]. Chengdu University of Technology; 2022.

38. Xu X, Yang G, Tan Y, et al. Factors influencing four decades of forest change in Guizhou Province, China. Land. 2020; 9(2): 31. doi: 10.3390/land12051004.

39. Johnson DD, Miller RF. Structure and development of expanding western juniper woodlands as influenced by two topographic variables. Forest Ecology and Management. 2006; 229(1): 7–15. doi: 10.1016/j.foreco.2006.03.008

40. Reed DN, Anderson TM, Dempewolf J, et al. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. Journal of Biogeography. 2009; 36(4): 770–782. doi: 10.1111/j.1365-2699.2008.02017.x

41. Fuller JL, Foster DR, Mclachlan JS, et al. Impact of Human Activity on Regional Forest Composition and Dynamics in Central New England. Ecosystems. 1998; 1(1): 76–95. doi: 10.1007/s100219900007




DOI: https://doi.org/10.24294/sf11558

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