OneForestKB: A Knowledge Base for Global Forest Data Curation and Exploitation Using Geospatial Services

Global forests are critical for carbon sequestration and climate change mitigation, local and international organizations have developed information systems to store and manage forestry data. Forestry data is heterogeneous, multi-scale, and multi-source, encompassing spatio-temporal observations and metadata about tree species and traits such as trunk diameter at breast height. However, this data often remains stored in local installations and is rarely shared as open data, thereby neglecting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. The Semantic Web (SW) community has contributed several standards in related areas such as phenotypic descriptions of species (OBO/PATO), spatial objects (GeoSPARQL), observations and measurements (SOSA), units of measurements (QUDT), among others. However, to our knowledge, comprehensive studies on how to arrange and combine these standards to support curation, reuse, and exploitation of forestry datasets are lacking. To address this gap, we propose the OneForest Knowledge Base (OneForestKB), built on a semantic profile and novel methods to validate and enrich forestry datasets. The semantic profile is designed to maximise the reuse of existing ontologies. We demonstrate its relevance through two use cases in the Amazonian forest of French Guiana, improving data quality via spatial validation rules implemented with SHACL and GeoSPARQL. The SHACL rules are generalized and shared for any GeoSPARQL-based system. We also develop forestry-specific data enrichment rules for calculating ecological indexes used in geographic regions as biodiversity proxies. OneForestKB is extensible, allowing new validations and inferences to be added for specific use cases.