作为新一代的地理分析工具，丰富的地理空间大数据为虚拟地理环境的发展带来了前所未有的契机。然而，伴随数据获取的逐渐便捷，虚拟地理环境普遍存在“可视”而“不智”的问题，还远未达到对现实地理环境可计算、可管理、可决策的需求。提高虚拟地理环境中数据的语义互操作性是解决该问题的关键。本研究首先提出了具有知识驱动、语义协同和结构统一等特点语义虚拟地理环境。然后阐述了语义虚拟地理环境可以利用知识图谱作为现实地理环境的形式表达，通过集成实时的感知数据，以及虚实之间的交互数据信息，形成现实地理环境在虚拟空间的映射。最后本文以湿地监测为例，基于知识图谱构建语义虚拟地理环境，通过湿地监测本体实现多要素、多源异构、多时空尺度的湿地监测数据语义整合，以直观的方式刻画复杂的湿地生态系统。利用关联的监测数据之间构建的丰富语义关系，实现湿地虚拟地理环境的知识化搜索和语义推理，从而为湿地的综合信息化管理决策提供支持。

The abundance of geospatial big data has created unprecedented opportunities for the development of virtual geographic environments (VGEs), but with the gradual ease of data acquisition, VGEs generally suffer from the problem of being "visual" but not "intelligent" and have yet to meet the demand for real-world geographic environments that are computable, manageable, and decision-ready. This paper first summarizes the challenges that geospatial big data pose to virtual geographic environments and proposes that improving the semantic interoperability of geospatial big data is the key to solving the aforementioned problems in virtual geographic environments. This involves using a machine-readable and human-understandable language to establish semantic relationships between data in a structured manner within virtual geographic environments. Therefore, the concept of Semantic Virtual Geographic Environments (SVGEs) is proposed. Semantic Virtual Geographic Environments use Semantic Web technologies such as ontologies and knowledge graphs to unify the description, management, and analysis of fragmented, unstructured, and non-explicitly related raw data generated by geographic objects and geographic processes in real-world environments with more standardized semantics, thus building a highly integrated digital geographic environment with data connectivity and interoperability. Semantic Virtual Geographic Environments are a type of virtual geographic environment that is semantically enhanced in the context of geospatial big data. SVGEs have characteristics such as knowledge-driven, semantic collaboration, and structural unity. Considering that the knowledge graph is the culmination of knowledge engineering in the era of big data, it can provide solutions for data knowledge organization and intelligent application in virtual geographical environment due to its powerful semantic expression ability, storage capacity and reasoning ability. Therefore, the semantic virtual geographic environment can use the knowledge graph as the formal expression of the real geographic environment, and form the mapping of the real geographic environment in the virtual space by integrating the real-time perception data and the interactive data information between the virtual and the real. Finally, this article takes wetland monitoring as an example and combines knowledge graph with virtual geographic environment to construct a semantic virtual geographic environment for wetland monitoring. Based on the characteristics of wetland monitoring data, conceptual modeling and formal expression of monitoring data were carried out, and a wetland monitoring ontology was constructed. The knowledge graph ontology can achieve a comprehensive, clear, and unambiguous description of monitoring data, laying the foundation for interoperability of monitoring data in virtual geographic environments. On the basis of wetland monitoring ontology, large-scale multi-source heterogeneous monitoring data is semantically mapped, achieving dual integration of isolated and scattered monitoring data in both logical and physical aspects in a virtual geographic environment. Then, the rich semantic relationships between monitoring data can be utilized to achieve knowledge-based search of monitoring data. In addition, the semantic virtual geographic environment also has strong inference ability, which can generate new knowledge through inference from native associated monitoring data, thus providing support for the comprehensive information management decision-making of wetlands. This study is a preliminary attempt to explore the theory, methods, and applications of semantic virtual geographic environments. However, further exploration is needed to truly achieve the full utilization of data in virtual geographic environments.