A GeoSOT-Based Position-Linked Identifier Framework for Individual Tree Management in Digital Twin Forests

High-resolution LiDAR and individual-tree modeling are generating increasing volumes of tree-level spatial data, including coordinates, tree height, and diameter at breast height (DBH). However, the lack of scalable and spatially explicit identifiers still limits the organization and integration of tree records in digital twin forest systems. This paper presents a GeoSOT-based framework for assigning position-linked identifiers to standardized tree observation records. The proposed code is used as a spatial anchor for record organization, candidate retrieval, and lifecycle-oriented management, rather than as a direct label of biological tree identity. The framework is implemented through a Yukon-based workflow for spatial storage and GeoSOT-code attachment, with a Bigtable-style schema described for time-stamped record organization. In a Mengjiagang forest farm case study, 604 treetop observations were extracted from airborne-LiDAR-derived canopy height models. Perturbation tests, boundary stress testing, controlled candidate matching, and a prototype retrieval benchmark show that fine-level GeoSOT codes are sensitive to positional uncertainty, whereas coarser levels combined with target-cell and adjacent-cell retrieval provide more stable candidate filtering with compact candidate sets under controlled experimental conditions. These results suggest that GeoSOT-based coding can support tree-observation record organization and candidate matching in digital twin forest systems. Independent cross-source identity validation and deployed database-level benchmarking should be addressed using real multi-source datasets and operational database environments.