Research on Land Use Change and the Contribution Degree of its Driving Force Factors: A Case Study of the Yangtze River Delta, China

Authors

  • Zhiren Zeng
  • Jiayu Wang

DOI:

https://doi.org/10.54097/m0jdp464

Keywords:

Enter ke Land use; Driving force factor; Random forest; The Yangtze River Delta; Spatio-temporal big data.

Abstract

 In recent decades, global land use change has had a profound impact on ecosystems, economic development and environmental quality. The study of land use change plays an important role in promoting the optimization of land resource management and urban planning and achieving the United Nations Sustainable Development Goals (SDGs). Based on the random forest method, this study classifies and interprets seven periods of remote sensing images in the Yangtze River Delta from 2000 to 2020, and systematically analyzes the spatio-temporal characteristics of land use change in this region and the contribution rate of its driving factors. From 2000 to 2020, the developed land increased significantly by 54.60%, while the areas of plowland, woodland and grassland decreased by 8.89%, 1.25% and 2.95% respectively. The areas of unused land and water bodies increased by 5.80% and 403.62% respectively. The analysis of the contribution degree of driving force factors indicates that natural factors such as precipitation, temperature and slope, along with socio-economic factors such as shopping services, medical services and catering services, jointly drive land use change. Among them, what is particularly prominent is that the contribution of precipitation to the expansion of woodland is as high as 0.164, and the contribution of the shopping service industry to the expansion of urban land is 0.149. It reveals the dual driving mechanism of "natural basis - humanistic traction" of land use change, providing a scientific basis for regional land resource management and sustainable development.

Downloads

Download data is not yet available.

References

[1] Ahmadzai, F. (2020). Analyses and modeling of urban land use and road network interactions using spatial-based disaggregate accessibility to land use. Journal of Urban Management, 9(3), 298-315.

[2] Allan, A., Soltani, A., Abdi, M. H., & Zarei, M. (2022). Driving forces behind land use and land cover change: A systematic and bibliometric review. Land, 11(8), 1222.

[3] Chen, M., Tan, Y., Xu, X., & Lin, Y. (2024). Identifying ecological degradation and restoration zone based on ecosystem quality: A case study of Yangtze River Delta. Applied Geography, 162, 103149.

[4] Chen, P., Luo, J., Xiong, Z., Wan, N., Ma, J., Yuan, J., & Duan, H. (2023). Can the establishment of a protected area improve the lacustrine environment? A case study of Lake Chaohu, China. Journal of Environmental Management, 342, 118152.

[5] Chen, Y., Zhang, L., Yan, M., Wu, Y., Dong, Y., Shao, W., & Zhang, Q. (2024). Spatiotemporal evolution and future simulation of land use/land cover in the Turpan-Hami Basin, China. Journal of Arid Land, 16(10), 1303-1326.

[6] Cheng, K., Su, Y., Guan, H., Tao, S., Ren, Y., Hu, T., . . . Guo, Q. (2023). Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 356-371.

[7] Deng, H., Shao, J., Wang, J., Gao, M., & Wei, C. (2016). Land use driving forces and its future scenario simulation in the Three Gorges Reservoir Area using CLUE-S model. Acta Geographica Sinica, 71(11), 1979-1997.

[8] Duan, L., Yang, S., Xiang, M., Li, W., & Li, J. (2024). Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China. Scientific Reports, 14(1), 23398.

[9] Gao, J., Zou, C., Zhang, K., Xu, M., & Wang, Y. (2020). The establishment of Chinese ecological conservation redline and insights into improving international protected areas. Journal of Environmental Management, 264, 110505.

[10] Hartog, H. d. (2021). Shanghai's regenerated industrial waterfronts: urban lab for sustainability transitions? Urban Planning, 6(3), 181-196.

[11] Kang, J., Xu, W., Yu, L., & Ning, Y. (2020). Localization, urbanization and globalization: Dynamic manufacturing specialization in the YRD mega-city conglomeration. Cities, 99, 102641.

[12] Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., . . . Barret, K. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.

[13] Lei, Z., Cai, S., Zhuo, S., Lau, Y.-y., & Lim, M. K. (2024). Analysis of the differences and spatial-temporal dynamic evolution of the environmental sustainability of the Yangtze River Economic Belt in China. International Journal of Shipping and Transport Logistics, 19(5), 1-41.

[14] Li, L., Li, G., Du, J., Wu, J., Cui, L., & Chen, Y. (2022). Effects of tidal flat reclamation on the stability of coastal wetland ecosystem services: A case study in Jiangsu Coast, China. Ecological Indicators, 145, 109697.

[15] Li, W., Wang, W., Chen, J., & Zhang, Z. (2022). Assessing effects of the Returning Farmland to Forest Program on vegetation cover changes at multiple spatial scales: The case of northwest Yunnan, China. Journal of Environmental Management, 304, 114303.

[16] Li, X., Zhang, X., Qiu, C., Duan, Y., Liu, S. a., Chen, D., . . . Zhu, C. (2020). Rapid loss of tidal flats in the Yangtze River Delta since 1974. International Journal of Environmental Research and Public Health, 17(5), 1636.

[17] Luo, H., Gao, X., Liu, Z., Liu, W., Li, Y., Meng, X., . . . Sun, L. (2024). Real-time characterization model of carbon emissions based on land-use status: A case study of Xi'an city, China. Journal of Cleaner Production, 434, 140069.

[18] Luo, H., Li, Y., Gao, X., Meng, X., Yang, X., & Yan, J. (2023). Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China. Applied Energy, 348, 121488.

[19] Luo, H., Wang, C., Li, C., Meng, X., Yang, X., & Tan, Q. (2024). Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China. Applied Energy, 360, 122819.

[20] Luo, H., Zhang, Y., Gao, X., Liu, Z., Meng, X., & Yang, X. (2024). Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China. Advances in Applied Energy, 16, 100197.

[21] Luo, H., Zhang, Y., Gao, X., Liu, Z., Song, X., Meng, X., & Yang, X. (2024). Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions. Energy, 305, 131722.

[22] Luo, H., Zhang, Y., Liu, Z., Yu, Z., Song, X., Meng, X., . . . Sun, L. (2024). Deciphering the point source carbon footprint puzzle: Land use dynamics and socio-economic drivers. Science of The Total Environment, 957, 176500.

[23] Matej, S., Weidinger, F., Kaufmann, L., Roux, N., Gingrich, S., Haberl, H., . . . Erb, K.-H. (2025). A global land-use data cube 1992–2020 based on the Human Appropriation of Net Primary Production. Scientific Data, 12(1), 511.

[24] McDonnell, J., Schunck, N., Higdon, D., Sarich, J., Wild, S., & Nazarewicz, W. (2015). Uncertainty quantification for nuclear density functional theory and information content of new measurements. Physical review letters, 114(12), 122501.

[25] Pati, S., Borah, A., Boruah, M. P., & Randive, P. R. (2022). Critical review on local thermal equilibrium and local thermal non-equilibrium approaches for the analysis of forced convective flow through porous media. International communications in heat and mass Transfer, 132, 105889.

[26] Piao, S., Yin, G., Tan, J., Cheng, L., Huang, M., Li, Y., . . . Peng, S. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global change biology, 21(4), 1601-1609.

[27] Rangel-Peraza, J. G., Sanhouse-García, A. J., Flores-González, L. M., Monjardín-Armenta, S. A., Mora-Félix, Z. D., Rentería-Guevara, S. A., & Bustos-Terrones, Y. A. (2024). Effect of land use and land cover changes on land surface warming in an intensive agricultural region. Journal of Environmental Management, 371, 123249.

[28] Ren, L., Song, S., & Zhou, Y. (2022). Evaluation of river ecological status in the plain river network area in the context of urbanization: A case study of 21 Rivers’ ecological status in Jiangsu Province, China. Ecological Indicators, 142, 109172.

[29] Roy, P. S., Ramachandran, R. M., Paul, O., Thakur, P. K., Ravan, S., Behera, M. D., . . . Kanawade, V. P. (2022). Anthropogenic land use and land cover changes—A review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing, 50(8), 1615-1640.

[30] Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79.

[31] She, Q., Cao, S., Zhang, S., Zhang, J., Zhu, H., Bao, J., . . . Liu, Y. (2021). The impacts of comprehensive urbanization on PM2. 5 concentrations in the Yangtze River Delta, China. Ecological Indicators, 132, 108337.

[32] Shen, D., Zhou, X., Xie, S., Lv, X., Peng, W., Wang, Y., & Wang, B. (2024). Paths and mechanisms of rural transformation promoted by rural collectively owned commercial construction land marketization in China. Land, 13(4), 416.

[33] Song, Y., Chen, B., Ho, H. C., Kwan, M.-P., Liu, D., Wang, F., . . . Xu, Y. (2021). Observed inequality in urban greenspace exposure in China. Environment International, 156, 106778.

[34] Sun, D., Zhou, L., Li, Y., Liu, H., Shen, X., Wang, Z., & Wang, X. (2017). New-type urbanization in China: Predicted trends and investment demand for 2015–2030. Journal of Geographical Sciences, 27, 943-966.

[35] Sun, L., Chen, J., Li, Q., & Huang, D. (2020). Dramatic uneven urbanization of large cities throughout the world in recent decades. Nature communications, 11(1), 5366.

[36] Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., & Liang, X. (2024). An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237, 121549.

[37] Tikuye, B. G., Rusnak, M., Manjunatha, B. R., & Jose, J. (2023). Land use and land cover change detection using the random forest approach: the case of the upper Blue Nile River Basin, Ethiopia. Global Challenges, 7(10), 2300155.

[38] Wang, Y., Zhang, W., Liu, X., Peng, H., Lin, M., Li, A., . . . Wang, L. (2025). A Deep Learning Method for Land Use Classification Based on Feature Augmentation. Remote Sensing, 17(8), 1398.

[39] Wang, Z., Li, X., Mao, Y., Li, L., Wang, X., & Lin, Q. (2022). Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China. Ecological Indicators, 134, 108499.

[40] Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475.

[41] Wu, H., Yang, C., Liang, A., Qin, Y., Dunchev, D., Ivanova, B., & Che, S. (2024). Urbanization and Carbon Storage Dynamics: Spatiotemporal Patterns and Socioeconomic Drivers in Shanghai. Land, 13(12), 2098.

[42] Xie, L., Wang, H., & Liu, S. (2022). The ecosystem service values simulation and driving force analysis based on land use/land cover: A case study in inland rivers in arid areas of the Aksu River Basin, China. Ecological Indicators, 138, 108828.

[43] Yang, F., Jiang, Y., Li, H., Hui, X., & Xing, S. (2024). Accurate model development for predicting sprinkler water distribution on undulating and mountainous terrain. Computers and Electronics in Agriculture, 224, 109196.

[44] Yang, R., Xu, Q., Xu, X., & Chen, Y. (2019). Rural settlement spatial patterns and effects: Road traffic accessibility and geographic factors in Guangdong Province, China. Journal of Geographical Sciences, 29, 213-230.

[45] Yin, R., Li, X., & Fang, B. (2023). The relationship between the spatial and temporal evolution of land use function and the level of economic and social development in the Yangtze River Delta. International Journal of Environmental Research and Public Health, 20(3), 2461.

[46] Yu, B. (2021). Ecological effects of new-type urbanization in China. Renewable and Sustainable Energy Reviews, 135, 110239.

[47] Yu, Z., Chen, L., Li, L., Zhang, T., Yuan, L., Liu, R., . . . Shi, S. (2021). Spatiotemporal characterization of the urban expansion patterns in the Yangtze River Delta region. Remote Sensing, 13(21), 4484.

[48] Yu, Z., Wang, Q., Xu, Y., Lu, M., Lin, Z., & Gao, B. (2022). Dynamic impacts of changes in river structure and connectivity on water quality under urbanization in the Yangtze River Delta plain. Ecological Indicators, 135, 108582.

[49] Zeng, P., Shi, D., Liu, Y., Tian, T., Che, Y., & Helbich, M. (2024). Parks may not be effective enough to improve the thermal environment in Shanghai (China) as our modified H3SFCA method suggests. Building and Environment, 253, 111291.

[50] Zhang, D., Wang, X., Qu, L., Li, S., Lin, Y., Yao, R., . . . Li, J. (2020). Land use/cover predictions incorporating ecological security for the Yangtze River Delta region, China. Ecological Indicators, 119, 106841.

[51] Zhang, J., Zheng, Y., Wen, T., Yang, M., & Feng, Q. m. (2022). The impact of built environment on physical activity and subjective well-being of urban residents: a study of core cities in the Yangtze River Delta survey. Frontiers in Psychology, 13, 1050486.

[52] Zhang, Q., Song, C., & Chen, X. (2018). Effects of China’s payment for ecosystem services programs on cropland abandonment: A case study in Tiantangzhai Township, Anhui, China. Land use policy, 73, 239-248.

[53] Zhang, W., Tian, J., Zhang, X., Cheng, J., & Yan, Y. (2023). Which land cover product provides the most accurate land use land cover map of the Yellow River Basin? Frontiers in Ecology and Evolution, 11, 1275054.

[54] Zhang, X., & Huang, X. (2019). Human disturbance caused stronger influences on global vegetation change than climate change. PeerJ, 7, e7763.

[55] Zhao, H., Chang, C., Wang, Z., & Zhao, G. (2025). A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features. Sensors, 25(2), 503.

[56] Zhou, Y., Huang, X., Chen, Y., Zhong, T., Xu, G., He, J., . . . Meng, H. (2017). The effect of land use planning (2006–2020) on construction land growth in China. Cities, 68, 37-47.

[57] Zhu, J., Wang, M., & Zhang, C. (2022). Impact of high-standard basic farmland construction policies on agricultural eco-efficiency: Case of China. Natl. Account. Rev, 4, 147-166.

Downloads

Published

10-06-2025

Issue

Section

Articles

How to Cite

Zeng, Z., & Wang, J. (2025). Research on Land Use Change and the Contribution Degree of its Driving Force Factors: A Case Study of the Yangtze River Delta, China. Journal of Innovation and Development, 11(3), 244-259. https://doi.org/10.54097/m0jdp464