{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:08:45Z","timestamp":1760144925338,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ATHOS Research Programme","award":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"],"award-info":[{"award-number":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"]}]},{"name":"INGV Volcanoes Department","award":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"],"award-info":[{"award-number":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"]}]},{"name":"Project INGV Pianeta Dinamico VT_ORME 2023\u20132025","award":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"],"award-info":[{"award-number":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"]}]},{"name":"Fulbright Visiting Research Program","award":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"],"award-info":[{"award-number":["INGV OB.FU. 0867.010","144\/2020","INGV OB.FU. 1020.010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA\u2019s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity.<\/jats:p>","DOI":"10.3390\/rs16112001","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T03:51:45Z","timestamp":1717386705000},"page":"2001","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3054-6840","authenticated-orcid":false,"given":"Claudia","family":"Corradino","sequence":"first","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Osservatorio Etneo, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2560-3185","authenticated-orcid":false,"given":"Arianna Beatrice","family":"Malaguti","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Osservatorio Etneo, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8911-9187","authenticated-orcid":false,"given":"Micheal S.","family":"Ramsey","sequence":"additional","affiliation":[{"name":"Department of Geology and Environmental Science of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5734-9025","authenticated-orcid":false,"given":"Ciro","family":"Del Negro","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Osservatorio Etneo, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"AC48","DOI":"10.4401\/ag-7972","article-title":"Validation of an integrated satellite-data-driven response to an effusive crisis: The April\u2013May 2018 eruption of Piton de la Fournaise","volume":"61","author":"Harris","year":"2019","journal-title":"Ann. 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