Creation of data cube for the analysis of wildfires in Cyprus using open access data 

Abstract

This study presents the actions that are currently been conducted through a demonstration project in the framework of the EXCELSIOR funded project, entitled “Capitalizing on the ERATOSTHENES Data Cube to support the development of the Fire Risk Prediction Model” between the ERATOSTHENES Centre of Excellence and the National Observatory of Athens. Wildfires detection is a major issue for authorities. There are various causes of fire events with the most common being human influence. A fire risk prediction model through the analysis of geo-environmental and climate data is important for early warning and fire management. An effective wildfire risk prediction and management depend on the up-to-date, spatial explicit representation of the environment, mainly focusing on the biomass and characteristics of live and dead vegetation, which is the primary factor influencing fire behaviour and risk. In this work, a dataset from multiple modalities, including road density, travellers, forest-agriculture interface, burned areas from historical fire events, metrological data, land cover, vegetation indices from data cube, is generated. These factors are selected based on their potential correlation with the unique characteristics of the area investigated, the historical fire events, and the availability of relevant data. Artificial intelligence and machine learning models can use this multimodal dataset to improve forest fire management. Specifically, the combination of data cubes, machine learning, and geospatial ontology-based data access (OBDA) technologies, allows for effective harmonization of diverse data sources, enhancing the accuracy and efficiency of fire risk computations. ACKNOWLEDGEMENTThe authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.