Abstract:
Global forests are critical for carbon sequestration and to face climate change challenges. Forests are distributed across the planet and both local and international organizations have led efforts to develop information systems to store and manage forestry data. Such data includes spatio-temporal observations, metadata about tree species and their traits, for example the trunk diameter at breast height. Forestry data is in nature heterogeneous, multi-scale, and multi-source. In practice, however, forest 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 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, there is a lack of comprehensive studies demonstrating how these standards can be arranged and combined to facilitate the curation, reuse, and exploitation of forestry datasets. To cope this gap, we propose the OneForest Knowledge Base OneForestKB that is based on a semantic profile and novel methods to validate and enrich forestry datasets. We follow a strategy of reusing existing ontologies as the definition of a semantic profile suggests. The relevance of this strategy is demonstrated through two use cases in the Amazonian forest of French Guiana, showing how to improve data quality through spatial validation rules based on the W3C constraint language, SHACL, combined with the OGC standard GeoSPARQL; the SHACL rules are generalised and shared to be applicable to any system using the GeoSPARQL model. Besides, this work provides a set of data enrichment rules relevant to forestry studies, which enable to enrich geographic regions with the calculation of ecological indexes as proxies of biodiversity. OneForestKB is designed to be extensible, allowing new validations and inferences to be added based on specific use cases.
