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Workflow of TIRVolcH. The top panel shows input data, followed by (Step 1) Initialisation, (Step 2) Hotspot Detection, and (Step 3) Outputs (i.e., parameters retrieval). Note, the dashed and grey shaded box in Step 2 indicates iteration of the loop (dashed arrow) until no (further) Candidate Alerts are detected (see text for details). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Workflow of TIRVolcH. The top panel shows input data, followed by (Step 1) Initialisation, (Step 2) Hotspot Detection, and (Step 3) Outputs (i.e., parameters retrieval). Note, the dashed and grey shaded box in Step 2 indicates iteration of the loop (dashed arrow) until no (further) Candidate Alerts are detected (see text for details). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Article
Full-text available
Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volca...

Citations

... We propose a workflow for the characterization of the surface thermal state and the identification of the main thermal patterns of volcanic areas by analyzing a time series of satellite-derived nighttime LST parameters through ICA. The proposed method therefore allowed for the investigation of thermal anomalous zones, extracting from TIR images the surface temperature rather than other radiative power trends, e.g., [98]. We applied this procedure to study the CFc volcanic area by considering 90 cloud-free nighttime L8 TIRS B10 images during the 2013-2022 time interval. ...
Article
Full-text available
In volcanic regions, the analysis of Thermal InfraRed (TIR) satellite imagery for Land Surface Temperature (LST) retrieval is a valid technique to detect ground thermal anomalies. This allows us to achieve rapid characterization of the shallow thermal field, supporting ground surveillance networks in monitoring volcanic activity. However, surface temperature can be influenced by processes of different natures, which interact and mutually interfere, making it challenging to interpret the spatio-temporal variations in the LST parameter. In this paper, we use a workflow to detect the main thermal patterns in active volcanic areas by analyzing the Independent Component Analysis (ICA) results applied to satellite nighttime TIR imagery time series. We employed the proposed approach to study the surface temperature distribution at the Campi Flegrei caldera volcanic site (Southern Italy, Naples) during the 2013–2022 time interval. The results revealed the contribution of four main distinctive thermal patterns, which reflect the endogenous processes occurring at the Solfatara crater, the environmental processes affecting the Agnano plain, the unique microclimate of the Astroni crater, and the morphoclimatic aspects of the entire volcanic area.