Review of simulations on land use change: a methodology based on bibliometric analysis
IntroductionLand use change simulation is crucial for understanding global environmental changes and guiding sustainable land management. This study conducts a bibliometric analysis of 2,147 Web of Science articles from 1988 to 2023 to summarize research trends, thematic evolutions, and future direc...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Sustainable Food Systems |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548565/full |
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| author | Qingquan Sun Qingquan Sun Lexuan Huang Han Meng Liang Chi Jianzhai Wu Xiangyang Zhou |
| author_facet | Qingquan Sun Qingquan Sun Lexuan Huang Han Meng Liang Chi Jianzhai Wu Xiangyang Zhou |
| author_sort | Qingquan Sun |
| collection | DOAJ |
| description | IntroductionLand use change simulation is crucial for understanding global environmental changes and guiding sustainable land management. This study conducts a bibliometric analysis of 2,147 Web of Science articles from 1988 to 2023 to summarize research trends, thematic evolutions, and future directions in land use change modeling.MethodsUsing Biblioshiny tools, the study applies quantitative analytics, co-citation network mapping, and keyword clustering.ResultsThe research reveals three developmental phases. From 1988 to 2000 (62 articles), foundational models like CLUE and CA were developed. During 2001–2016 (1,039 articles), there were advancements in coupled models and multi-scenario simulations. From 2017 to 2023 (1,046 articles), the focus shifted to integrative frameworks linking land dynamics, ecosystem services, and climate feedbacks. Annual publication outputs increased from 5 to 149, showing exponential growth. Key research themes involve computational modeling, spatiotemporal dynamics analysis, and environmental impact assessment. Recent trends highlight “river-basin,” “multi-source data fusion,” and “geographically weighted models,” indicating a move toward basin-scale simulations, machine learning integration, and policy-oriented scenarios. China, the U.S., and Germany lead in research output, with top institutions including Beijing Normal University and the Chinese Academy of Sciences. China and the U.S. have strong domestic collaborations, while European countries have higher international collaboration ratios.DiscussionThe analysis points out research gaps, such as limited integration of socio-economic drivers and insufficient cross-scale modeling. Future research should focus on developing hybrid frameworks combining process-based and data-driven models, leveraging multi-source data for accuracy, and designing scenario-based models for sustainable development goals, especially in river basins and urbanizing regions. |
| format | Article |
| id | doaj-art-4ec9d414009a4ae4a443af76757a79e5 |
| institution | Kabale University |
| issn | 2571-581X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj-art-4ec9d414009a4ae4a443af76757a79e52025-08-20T03:24:36ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2025-06-01910.3389/fsufs.2025.15485651548565Review of simulations on land use change: a methodology based on bibliometric analysisQingquan Sun0Qingquan Sun1Lexuan Huang2Han Meng3Liang Chi4Jianzhai Wu5Xiangyang Zhou6Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, ChinaIntroductionLand use change simulation is crucial for understanding global environmental changes and guiding sustainable land management. This study conducts a bibliometric analysis of 2,147 Web of Science articles from 1988 to 2023 to summarize research trends, thematic evolutions, and future directions in land use change modeling.MethodsUsing Biblioshiny tools, the study applies quantitative analytics, co-citation network mapping, and keyword clustering.ResultsThe research reveals three developmental phases. From 1988 to 2000 (62 articles), foundational models like CLUE and CA were developed. During 2001–2016 (1,039 articles), there were advancements in coupled models and multi-scenario simulations. From 2017 to 2023 (1,046 articles), the focus shifted to integrative frameworks linking land dynamics, ecosystem services, and climate feedbacks. Annual publication outputs increased from 5 to 149, showing exponential growth. Key research themes involve computational modeling, spatiotemporal dynamics analysis, and environmental impact assessment. Recent trends highlight “river-basin,” “multi-source data fusion,” and “geographically weighted models,” indicating a move toward basin-scale simulations, machine learning integration, and policy-oriented scenarios. China, the U.S., and Germany lead in research output, with top institutions including Beijing Normal University and the Chinese Academy of Sciences. China and the U.S. have strong domestic collaborations, while European countries have higher international collaboration ratios.DiscussionThe analysis points out research gaps, such as limited integration of socio-economic drivers and insufficient cross-scale modeling. Future research should focus on developing hybrid frameworks combining process-based and data-driven models, leveraging multi-source data for accuracy, and designing scenario-based models for sustainable development goals, especially in river basins and urbanizing regions.https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548565/fullland use changebibliometricspredictionsimulationmodels |
| spellingShingle | Qingquan Sun Qingquan Sun Lexuan Huang Han Meng Liang Chi Jianzhai Wu Xiangyang Zhou Review of simulations on land use change: a methodology based on bibliometric analysis Frontiers in Sustainable Food Systems land use change bibliometrics prediction simulation models |
| title | Review of simulations on land use change: a methodology based on bibliometric analysis |
| title_full | Review of simulations on land use change: a methodology based on bibliometric analysis |
| title_fullStr | Review of simulations on land use change: a methodology based on bibliometric analysis |
| title_full_unstemmed | Review of simulations on land use change: a methodology based on bibliometric analysis |
| title_short | Review of simulations on land use change: a methodology based on bibliometric analysis |
| title_sort | review of simulations on land use change a methodology based on bibliometric analysis |
| topic | land use change bibliometrics prediction simulation models |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2025.1548565/full |
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