WMO: an ontology for the semantic enrichment of wetland monitoring data

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetl...

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Bibliographic Details
Published in:	International journal of digital earth 2023-12, Vol.16 (1), p.2189-2211
Main Authors:	Xiao, Xin, Lin, Hui, Fang, Chaoyang
Format:	Article
Language:	eng
Subjects:	Aquatic ecosystems Context Data acquisition Disasters Ecological monitoring Emergency preparedness Emergency response Interoperability knowledge graph Monitoring Natural disasters Ontology semantic interoperability Semantics Sensors spatiotemporal data wetland monitoring Wetlands
Online Access:	Get full text
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Summary:	Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO's usability and reusability.
ISSN:	1753-8947 1753-8955