Abstract
Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.
Highlights
Modern geoscience is a typical data-intensive science
For multiple-ontology ontology-based data integration (OBDI), each integrated data source will define a local ontology, and the purpose of integration is to align these ontologies with one another using semantic mappings
The disadvantage of this approach is the semantic mappings among involved ontologies are difficult to define and maintain due global ontology is difficult when the source data changes
Summary
Modern geoscience is a typical data-intensive science. Land-cover data, one of the big data of geosciences, are an important foundation to support scientific research. Global land-cover (GLC) products are important basic data for international initiatives, such as the United Nations Framework Convention on Climate. Sustainable Development Goals and the Kyoto Protocol, as well as for monitoring environmental change and global change research by governments and the scientific community [2]. With the development in recent years of open satellite archives and cloud computing platforms such as the Google Earth engine [13], the number of landcover data sources and the amount of generated data have increased continuously. In a review of global aquatic land-cover products [14], the statistical results show that about
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