A Statistical Approach to Satellite Time Series Analysis to Detect Changes in Thermal Activities: The Vulcano Island 2021 Crisis

Abstract

Vulcano belongs to the seven volcanic islands forming the Aeolian archipelago (Italy) and has the privilege to define an eruptive style as “Vulcanian”. It has to be considered as an active volcano as its most recent activity demonstrated. Starting by late spring 2021, the thermal state of the Vulcano summit area changed and the gas emission increased. During the summer and, in particular, starting from September, geophysical and geochemical signals, precisely those linked to the activity of the hydrothermal system that feeds the fumaroles of the Fossa crater, varied. The temperature of the gases emitted by the fumaroles on the crater rim has increased and the composition of the gases has showed an increase in CO2 and SO2 (carbon dioxide and sulfur dioxide) concentration. For such reasons, the authors decided to follow this event by analyzing the remotely sensed available data suitable for detecting changes in thermal state. By processing the TIRS (Landsat 8) and ASTER time series, two long-term surface temperature logs were obtained and, therefore, by adopting a statistical approach, an analysis in both space and time domains has emphasized a thermal signature since mid-September 2021.

Machine Learning Techniques in Predicting Bottom Hole Temperature and Remote Sensing for Assessment of Geothermal Potential in the Kingdom of Saudi Arabia

“…Indeed, present-day volcanoes release relatively very small quantities of CO 2 compared to land-use changes [64,65]. An increase in geochemical parameters, specifically the LST as well as CO 2 emission within the volcanic active area, were observed [66].…”

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

“…Since, in previous work [15], the SMW algorithm in GEE is applied only for a single daytime image, to increase the density of the analyzed collection, it was necessary to readapt the code in [22] for the LST retrieval in the nighttime by adopting ASTER-GED emissivity. When the main energy source is the Sun's radiation (during daytime), the surface emissivity is computed by starting from the FVC retrieval and the VCM method due to the correlation between the emissivity value and the vegetation cover on the surface [44]. To obtain the FVC, in [22], the NDVI was computed from the Surface Reflectance L8 collection band 4 and band 5.…”

A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images

“…Thus, an evident indicator of volcanic activity is the high surface temperature compared to the ambient temperature of the ground. Hence, identifying volcanic thermal activity in satellite images is relatively simple; it is sufficient to distinguish the pixels that exhibit thermal emissions directly associated with volcano activity from neighboring pixels that do not [41]. This approach allows not only for the detection of volcanic unrest based on the occurrence of thermal anomalies at a specific time (e.g., [17]), but also for the use of thermal data to retrieve such parameters as the effusion rate of lava flows (e.g., [42][43][44]) and the areal coverage of hot volcanic deposits (e.g., [45]).…”