A Unified Spatio-Temporal Data Organization Model for Forest and Grassland Resources Based on GeoSOT Encoding

Abstract

The efficient integration, management, and application of multi-source heterogeneous spatio-temporal data remain a critical challenge in forest and grassland ecological informatics. Traditional GIS-based approaches often suffer from limited scalability, poor adaptability to diverse data modalities, and inadequate support for time-space linkage. To address these limitations, this study proposes a novel spatio-temporal data organization model based on the Geographical Subdivision Grid with One-dimension-integer on Two to n-th power (GeoSOT) encoding framework. We introduce a unified three-domain identifier—composed of spatial code, temporal stamp, and semantic attributes—to support fine-grained partitioning and indexing of both structured (e.g., vector, raster) and unstructured (e.g., video, sensor logs, text) data. The organization model employs multi-level GeoSOT grid cells as spatial anchors, integrating temporal semantics and object-level identifiers to form a one-code-per-element schema, ensuring the uniqueness and traceability of each data entity. A prototype system was implemented using forest resource and fire monitoring datasets from the Asia-Pacific Forestry Center. Comprehensive experiments demonstrate that the proposed model significantly improves data fusion flexibility, retrieval efficiency, and query precision compared to conventional spatial database models. Moreover, the system enables scalable and interactive spatio-temporal queries across multi-modal data sources. This study contributes a generalized, extensible, and semantically rich data organization framework that bridges spatial and temporal dimensions in forest and grassland applications. It holds promise for large-scale ecological monitoring, forest fire early warning, and smart forestry governance. Future work will focus on extending the model to real-time streaming data and integrating intelligent analytics for enhanced decision support.