Integration of Environmental Models in Spatial Data Infrastructures: A Use Case in Wildfire Risk Prediction

Abstract

Achieving sustainable growth in our society implies monitoring our environment in order to measure human impact and detect relevant changes and detrimental driving factors such as wildfires and desertification. In order for experts to perform environmental modelling they need to be able to access data and models in an efficient and interoperable manner as well as share their findings to assist other professionals in decision making. In the current information society, distributed information systems are essential for sharing digital resources such as data and tools. Advances in Service-Oriented Architectures (SOA) allow for the distribution and accessibility of on-line resources such as data and tools, which served through standards-based services improve the sharing of data, models and models results. This research presents a service-oriented application that addresses the issues of interoperable access to environmental modelling capabilities as well as the mechanisms to share their results an efficiently throughout interoperable components. The aim is twofold, first we present different models for multi-scale forest fire risk prediction based on spatial point processes, and second we provide this functionality as a distributed application, that, based on international standards, such as those offered by the Open Geospatial Consortium (OGC), improves interoperable access to these models as well as the publication of the results to be shared with other interested stakeholders.