Abstract
To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001–2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model’s accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.
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Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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The authors thank editor-in-chief Dr. Philippe Garrigues, editorial assistants Fanny Creusot and Giulia Marinaccio, and three reviewers for their critical comments and valuable suggestions.
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This work was supported by the National Natural Science Foundation of China (No. 41907228), Chengdu Science and Technology Program (2022-YF05-00340-SN), Sichuan Science and Technology Program, China (No. 2020YFS0297), and the Fundamental Research Funds for the Central Universities (No. 2682020CX11).
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Huang, J., Wu, X., Ling, S. et al. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020. Environ Sci Pollut Res 29, 86954–86993 (2022). https://doi.org/10.1007/s11356-022-23732-z
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DOI: https://doi.org/10.1007/s11356-022-23732-z