The purpose of this study is to analyze the surface temperature and the distribution of thermal signatures on Tuscany’s geothermal districts using data obtained through three separate surveys via satellite and an unmanned aerial vehicle (UAV). The analysis considers the highest available spatial resolution ranging from hundreds of meters per pixel of the satellite thermal images and the tenths/hundreds of centimeters per pixel of the thermal images acquired by the UAV. The surface temperature maps obtained by satellite data acquired at suitable spatial resolution and the thermal measurements obtained by the thermal camera installed on the UAV were orthorectified and geocoded. This allowed, for example, following the evolution of thermal anomalies, which may represent a modification of the current state of the geothermal field and a possible hazard for both the population and industrial assets. Here, we show the results obtained in three field campaigns during which the simultaneous acquisition of Landsat 8 satellite and UAV (FlyBit octocopter, IDS, Rome, Italy) thermal data were analyzed. By removing the atmosphere contribution from Landsat 8 data, we have produced three surface temperature maps that are compared with the ground field measurements and the surface temperature maps elaborated by FLIR VUE PRO-R on the UAV.
Quiescent volcanoes dissipate a large part of their thermal energy through hot soils and ground degassing mainly in restricted areas called Diffuse Degassing Structures. La Solfatara crater represents the main spot of thermal release for the Campi Flegrei volcano (Italy) despite its reduced dimensions with regards to the whole caldera. The purpose of this study was to develop a method to measure thermal energy release extrapolating it from the ground surface temperature. We used imaging from thermal cameras at short distances (1 m) to obtain a mapping of areas with thermal anomalies and a measure of their temperatures. We built a conceptual model of the energy release from the ground to atmosphere, which well fits the experimental data taken in the La Solfatara crater. Using our model and data, we could estimate the average heat flux in a portion of the crater as q a v g = 220 ± 40 W / m 2 , compatible with other measurements in literature.
Open conduit volcanoes like Stromboli can display elusive changes in activity before major eruptive events. Starting on December 2020, Stromboli volcano displayed an increasing eruptive activity, that on 19 May 2021 led to a crater-rim collapse, with pyroclastic density currents (PDCs) that spread along the barren NW flank, entered the sea and ran across it for more than 1 km. This episode was followed by lava flow output from the crater rim lasting a few hours, followed by another phase of lava flow in June 2021. These episodes are potentially very dangerous on island volcanoes since a landslide of hot material that turns into a pyroclastic density current and spreads on the sea surface can threaten mariners and coastal communities, as happened at Stromboli on 3 July and 28 August 2019. In addition, on entering the sea, if their volume is large enough, landslides may trigger tsunamis, as occurred at Stromboli on 30 December 2002. In this paper, we present an integration of multidisciplinary monitoring data, including thermal and visible camera images, ground deformation data gathered from GNSS, tilt, strainmeter and GBInSAR, seismicity, SO2 plume and CO2 ground fluxes and thermal data from the ground and satellite imagery, together with petrological analyses of the erupted products compared with samples from previous similar events. We aim at characterizing the preparatory phase of the volcano that began on December 2020 and led to the May–June 2021 eruptive activity, distinguishing this small intrusion of magma from the much greater 2019 eruptive phase, which was fed by gas-rich magma responsible for the paroxysmal explosive and effusive phases of July–August 2019. These complex eruption scenarios have important implications for hazard assessment and the lessons learned at Stromboli volcano may prove useful for other open conduit active basaltic volcanoes.
Thermal camera use is becoming ever more widespread in volcanic and environmental research and monitoring activities. Depending on the scope of an investigation and on the type of thermal camera used, different software for thermal infrared (IR) images analysis is employed. The Osservatorio Vesuviano Sezione in Napoli of the Istituto Nazionale di Geofisica e Vulcanologia (INGV-OV) processes the images acquired during thermal monitoring activities acquired in the Neapolitan areas (Vesuvio, Ischia and Campi Flegrei) with different FLIR software that returns for each image, or for each selected area within the image, a series of parameters (maximum temperature, average temperature, standard deviation, etc.). An operator selects the area of interest and later “manually” inserts the relevant parameters in Excel sheets to generate graphs. Such a tedious, time- and resource-consuming procedure gave reason to implement a software able to automatically analyze sets of thermal images taken with a handheld thermal camera without any manual action. This paper describes the method and the software implemented to “automate” and refine the extrapolation process and the analysis of the relevant information. The employed method clusters thermal images by applying K-MEANS and DBSCAN techniques. After clustering a series of images, the software displays the necessary statistics to highlight possible fluctuations in temperature values. The software, “StaTistical Analysis clusteRed ThErmal Data” (STARTED), is already available. Although it has been developed mostly to support monitoring of the volcanoes in Campania, it is quite versatile and can be used for any activity that implies thermal data analysis. In this paper, we describe the workflow and the dataset used to develop the software, as well as the first result obtained from it.
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