We hereby present VolcFire, a new cellular automaton model for fire propagation aimed at the creation of fire hazard maps for fires of volcanic origin. The new model relies on satellite-derived input data for the topography, land-use, fuel, and humidity information, and produces probabilistic maps of fire propagation simulating fire spread. The model contains several simplifications compared to the current state-of-the-art, limiting its usability to plan fire-fighting interventions during an event in favour of a reduced computational load. The accuracy and reliability of the model are also discussed by presenting its ability to reproduce two recent fires on Stromboli island, with good spatial fit (Brier score of $0.146\pm 0.002$ for the 3 July 2019 volcanic fire, and of $0.073\pm 0.001$ for the 25 May 2022 anthropogenic fire) and less than $1.5\%$ variation across multiple simulations for the same event.

Deception Island is one of the most active and best‐documented volcanoes in Antarctica. Since its last eruption in 1970, several geophysical surveys have targeted reconstructing its magmatic systems. However, geophysics fails to reconstruct the pathways magma and fluids follow from depth to erupt at the surface. Here, novel data selection strategies and multi‐frequency absorption inversions have been framed in a Geographical Information System, using all available geological (vents and faults distribution), geochemical and geophysical knowledge of the volcano. The result is the detection of these eruptive pathways. The model offers the first image of the magma and associated fluids pathways feed the 1967, 1969, and 1970 eruptions. Results suggest that future ascending paths might lead to active research bases and zones of planned helicopter rescue. The connection between seismic absorption, temperature, and fluid content makes it a promising attribute for detecting and monitoring eruptions at active calderas.

Automated early warning systems for volcanoes, capable of early recognition of any signs of impending eruption, as well as to track the evolution of different kinds of eruptive activity in near real time, are essential to assess the volcanic hazards and mitigate the associated risk. Satellite imagery offers a systematic, synoptic framework for monitoring active volcanoes in even the most isolated corners of the Earth. Here we have applied a machine learning technique to automatically classify a pixel in SEVIRI imagery over an active volcano, in order to detect and characterize its eruptive activity. In particular, we have discriminated against five main classes of pixels: clear sky, cloud-contaminated, ash-contaminated, SO2-contaminated and thermal anomalies.Due to the enormous amount of data and the difficulty in labeling it, we have used self-supervised learning to study data acquired since 2004. We have selected an area of 5x5 pixels around the active volcano under study and followed the spectral radiance variation of each pixel in the twelve bands availables. Our technique has been applied to several active volcanoes within the SEVIRI disk, including Etna, Nyiragongo, Stromboli, Nabro, La Palma and Fogo.

On 3 July 2019, paroxysmal activity at the Stromboli volcano ignited a fire in the south-western sector of the island. This fire burnt the entire southern and eastern area. Thanks to the intervention of the firefighters, the fire did not spread northward, thus avoiding further damage. What could have happened if there had been no intervention? Would the fire have stopped its spread on its own, or would it have burnt the entire island? We show through numerical simulations what could have happened if the fire had not been put out by firefighters and how numerical modelling can help plan preventive actions to avoid worst-case scenarios.

Between 2019 and 2022, multiple fires affected Stromboli Island, causing significant environmental damage and highlighting the need for effective preventive measures. In 2019, two fires were ignited by eruptive activity, while in 2022, human actions were responsible for a major wildfire. These events underline the complexity of fire dynamics in volcanic environments, where topography, wind, and vegetation flammability play critical roles in fire propagation. Furthermore, the aftermath of these fires has triggered secondary hazards, such as floods and debris flows following heavy rains, which further exacerbated the environmental and societal impact on the island.In this work we leverage a cellular automata-based numerical model specifically designed for volcanic-induced fires, integrating factors such as wind, topography, and vegetation characteristics to simulate the fire evolution as well as the interaction with possible mitigations measurements. We conducted fire spreading numerical simulations exploring different configurations of firebreak lines, including the strategic use of hiking trails as potential barriers to fire spread. This model also benefits from the integration of Geographic Information Systems (GIS), enhancing its ability to classify soil types and map burnt areas with good spatial accuracy.The simulations demonstrate how the proposed model can be used to create fire hazard scenarios and evaluate the effectiveness of various mitigation strategies. For instance, our results for the 2019 and 2022 fires exhibit high spatial accuracy, with Brier scores of 0.188±0.002 and 0.073±0.001, respectively. These findings underscore the utility of numerical modeling not only for understanding fire dynamics but also for planning preventive actions to mitigate wildfire risks and reduce the cascading effects of secondary hazards, ultimately contributing to more effective fire and disaster management on Stromboli and other volcanoes with similar environmental conditions.