Integrating Image and Spatial Data for Biodiversity Information Management

Biologists gather many kinds of data for biodiversity studies; these data are managed by distinct types of information systems. GIS-based biodiversity systems support sophisticated spatial correlations on living beings and their habitats, and spatio-temporal ecosystem modeling. Image information systems allow content-based image retrieval, to help species identification based on similarity (e.g., shape and color characteristics). Different kinds of rule-based systems support species characterization. Unfortunately, these systems (and the underlying data) are independent of each other. This paper presents a solution that seamlessly combines these functionalities, supporting queries that merge textual descriptions, spatial correlations and content-based predicates. The solution is being implemented at Virginia Tech, for identification and data retrieval, supporting management of fish species. It takes advantage of innovations in Digital Library technology to combine networked collections of heterogeneous data under integrated management.

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