Abstract
Alterations in land use and cover often stem from human activities, which play a significant role in global change. Scholars have delved deeply into the evolution and prediction of land use, yielding fruitful research outcomes. However, there is a lack of quantitative and comprehensive evaluations. This study uses CiteSpace bibliometric software to map the collaboration networks and keyword co-occurrence of relevant literature from the Web of Science (WOS) database from 2013 to 2022, revealing the evolution trends of academic collaboration and research hotspots in this field. This research fills the gap in the lack of quantitative analysis and comprehensive evaluation of the research outcomes in the field of land use evolution and prediction, providing a systematic and intuitive understanding of its research progress and frontiers. The research results indicate that: (1) Land use evolution and prediction literature volume and depth have increased over the past few years, and its development speed has accelerated substantially since 2017. (2) There is a concentration of research institutions and clear regional characteristics of their distribution. A research network has been established between the developed and developing countries. (3) The current research focuses on the driving factors of land use evolution, the prediction method model and the ecological environment impact brought by land use change, and the research heat continues to rise. For example, CA-Markov model and CLUE-S model are used to predict and simulate the spatial and temporal dynamics of land use change. (4) The future research trend is to predict possible future changes in land use by developing different future scenarios, including climate change, policy interventions and economic development. Based on bibliometric analysis, this paper provides in-depth insight and scientific basis for land resource management in the context of global change.





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We would like to acknowledge the reviewers for their helpful comments on this paper. This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.
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Lu Che: Conceptualization, Data curation, formal analysis, Methodology, Resources, Validation, Writing of the Original Draft. Sidai Guo: Data curation, Methodology, Project administration, Resources, Validation, Writing, review, and editing. Yuan Deng: Software, Data curation, Formal analysis, Writing—Review & Edit.
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Che, L., Guo, S. & Deng, Y. Land use evolution and prediction: a bibliometric review. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05983-0
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DOI: https://doi.org/10.1007/s13762-024-05983-0