Abstract: Three-dimensional laser scanning technology has become a crucial method for obtaining high-precision forest stand parameters in forestry surveys. However, the expression of forest three-dimensional scenes based on massive laser point clouds relies on professional software for processing, and it primarily focuses on object-level semantic understanding, lacking explicit expression of element relationship information. To address this issue, a three-dimensional scene graph conceptual model oriented to forest scenes was proposed, and the methods of element hierarchical classification, geometric description,semantic expression, and relationship description of forest scenes were systematically introduced. On this basis, the three-dimensional scene graph was combined with semantic association analysis, and a large language model was introduced as a query and analysis tool. A three-level universal evaluation framework was designed to comparatively evaluate ChatGPT-4o, DeepSeek-R1, and Grok4 in terms of operating speed, input limitations, and visual effects, and the efficacy, potential, and limitations of this model were discussed. Experimental results based on the public point cloud dataset ForestSemantic show that the three-dimensional scene graph has a good data-carrying capacity in forest scenes and can effectively organize and connect forest relationships; the accuracy of Grok4 is above 90%,which is superior to the other two large language models. This study can provide assistance for the spatial expression and analysis, operation management, and decision-making of forestry resources.

Key words: light detection and ranging, forestry survey, three-dimensional scene graph, large language model, forest spatial relation, forestry application