The advent of Internet of Things (IoT) technology has the potential to generate a huge amount of heterogeneous data at different geographical locations and with various temporal resolutions in environmental science. In many other areas of IoT deployment, volume and velocity dominate, however in environmental science, the more general pattern is quite distinct and often variety dominates. There exists a large number of small, heterogeneous and potentially complex datasets and the key challenge is to understand the interdependencies between these disparate datasets representing different environmental facets. These characteristics pose several data challenges including data interpretation, interoperability and integration, to name but a few, and there is a pressing need to address these challenges. The author postulates that Semantic Web technologies and associated techniques have the potential to address the aforementioned data challenges and support environmental science. The main goal of this thesis is to examine the potential role of Semantic Web technologies in making sense of such complex and heterogeneous environmental data in all its complexity.
The thesis explores the state-of-the-art in the use of such technologies in the context of environmental science. After an in-depth assessment of related work, the thesis further examined the characteristics of environmental data through semi-structured interviews with leading experts. Through this, three key research challenges emerge: discovering interdependencies between disparate datasets, geospatial data integration and reasoning, and data heterogeneity. In response to these challenges, an ontology was developed that semantically enriches all sensor measurements stemmed from an experimental Environmental IoT infrastructure. The resultant ontology was evaluated through three real-world use-cases derived from the interviews. This led to a number of major contributions from this work including: the development of an ontology tailored for streaming environmental data offering semantic enrichment of IoT data, support for spatio-temporal data integration and reasoning, and the analysis of unique characteristics of environmental science around data.