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人工智能在自然保护地生态游憩监测与管理中的应用研究进展

Research progress on the application of artificial intelligence in ecotourism monitoring and management in natural protected areas

  • 摘要:
    目的 采用文献计量与内容分析相结合的方法,从技术路径、应用领域、研究主题和前沿热点等维度,系统梳理人工智能在自然保护地生态游憩监测与管理中的研究脉络和发展趋势,为未来人工智能技术在自然保护地监测与管理领域的技术集成和管理模式创新提供理论支持和实践启示。
    方法 基于Web of Science和CNKI数据库中2005—2024年相关文献,利用CiteSpace软件进行可视化分析和国内外研究趋势比较。
    结果 ① 人工智能在自然保护地生态游憩监测与管理中的研究呈指数增长趋势,可分为技术萌芽辅助监管的探索期(2005—2016年)、技术积累提升精度的发展期(2017—2020年)、技术融合深化认知的成熟期(2021年至今)。② 人工智能在自然保护地监测与管理中的应用集中于游客流量管理、游客行为识别与类型划分及生态适宜性与承载力评估三大方向。③ 国内外在研究方法、主题内容上有所差异,国内研究主题聚焦于生态环境影响,采用定量与定性相结合的研究方法;国外研究则注重游憩体验优化,主要采用建模等定量研究方法。
    结论 未来人工智能在自然保护地生态游憩监测与管理中的研究应扩展至不同类型的自然保护地,加强理论模型构建,推动智慧化自然保护地的理论与实践相结合。

     

    Abstract:
    Objectives This study employed a combined approach of bibliometric analysis and content analysis to systematically review the research trajectory and development trends of artificial intelligence (AI) in the ecological recreation monitoring and management of natural protected areas, examining dimensions such as technical pathways, application domains, research themes, and frontier hotspots. The aim is to provide theoretical support and practical insights for the technological integration and management model innovation of AI in the monitoring and management of natural protected areas.
    Methods Based on relevant literature from the Web of Science and CNKI databases spanning 2005 to 2024, CiteSpace software was utilized for visual analysis and comparative examination of domestic and international research trends.
    Results The results showed that: 1) Research on AI in ecotourism monitoring and management of natural protected areas exhibited an exponential growth trend, which can be categorized into three phases: an exploratory period characterized by nascent technology and auxiliary supervision (2005–2016), a developmental period marked by technological accumulation and precision enhancement (2017–2020), and a maturation period defined by technology integration and deepened cognition (2021–present). 2) The application of AI in natural protected area monitoring and management was concentrated in three major directions: visitor flow management, visitor behavior recognition and typology classification, and assessment of ecological suitability and carrying capacity. 3) Differences existed between domestic and international research in terms of methodological approaches and thematic content. Domestic research focused on the impact on the ecological environment and adopted a combination of quantitative and qualitative research methods, while international research places greater emphasis on optimizing recreational experiences and mainly use quantitative research methods such as modeling.
    Conclusions Future research on AI in ecotourism monitoring and management of natural protected areas should expand to encompass diverse types of natural protected areas, strengthen the construction of theoretical models, and promote the integration of theory and practice in the development of intelligent natural protected area systems.

     

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