{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T13:59:54Z","timestamp":1775311194092,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ATHOS Research Programme","award":["OB.FU. 0867.010"],"award-info":[{"award-number":["OB.FU. 0867.010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Volcanic thermal anomalies are monitored with an increased application of optical satellite sensors to improve the ability to identify renewed volcanic activity. Hotspot detection algorithms adopting a fixed threshold are widely used to detect thermal anomalies with a minimal occurrence of false alerts. However, when used on a global scale, these algorithms miss some subtle thermal anomalies that occur. Analyzing satellite data sources with machine learning (ML) algorithms has been shown to be efficient in extracting volcanic thermal features. Here, a data-driven algorithm is developed in Google Earth Engine (GEE) to map thermal anomalies associated with lava flows that erupted recently at different volcanoes around the world (e.g., Etna, Cumbre Vieja, Geldingadalir, Pacaya, and Stromboli). We used high spatial resolution images acquired by a Sentinel-2 MultiSpectral Instrument (MSI) and a random forest model, which avoids the setting of fixed a priori thresholds. The results indicate that the model achieves better performance than traditional approaches with good generalization capabilities and high sensitivity to less intense volcanic thermal anomalies. We found that this model is sufficiently robust to be successfully used with new eruptive scenes never seen before on a global scale.<\/jats:p>","DOI":"10.3390\/rs14174370","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Data-Driven Random Forest Models for Detecting Volcanic Hot Spots in Sentinel-2 MSI Images"],"prefix":"10.3390","volume":"14","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-3972","authenticated-orcid":false,"given":"Eleonora","family":"Amato","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Osservatorio Etneo, 95125 Catania, Italy"},{"name":"Department of Mathematics and Computer Science, University of Palermo, 90123 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7066-6508","authenticated-orcid":false,"given":"Federica","family":"Torrisi","sequence":"additional","affiliation":[{"name":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania, Osservatorio Etneo, 95125 Catania, Italy"},{"name":"Department of Electrical, Electronic and Computer Engineering, University of Catania, 95131 Catania, Italy"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Harris, A. (2013). Thermal Remote Sensing of Active Volcanoes: A User\u2019s Manual, Cambridge University Press.","DOI":"10.1017\/CBO9781139029346"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111426","DOI":"10.1016\/j.rse.2019.111426","article-title":"How the variety of satellite remote sensing data over volcanoes can assist hazard monitoring efforts: The 2011 eruption of Nabro volcano","volume":"236","author":"Ganci","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1144\/SP426.21","article-title":"HOTSAT: A multiplatform system for the thermal monitoring of volcanic activity using satellite data","volume":"426","author":"Ganci","year":"2016","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1029\/90JB01392","article-title":"Combined use of visible, reflected infrared, and thermal infrared images for mapping Hawaiian lava flows","volume":"96","author":"Abrams","year":"1991","journal-title":"J. Geophys. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Corradino, C., Ganci, G., Bilotta, G., Cappello, A., Del Negro, C., and Fortuna, L. (2019). Smart decision support systems for volcanic applications. Energies, 12.","DOI":"10.3390\/en12071216"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1144\/SP426.17","article-title":"Operational thermal remote sensing and lava flow monitoring at the Hawaiian Volcano Observatory","volume":"426","author":"Patrick","year":"2016","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4401\/ag-7792","article-title":"Satellite-driven modeling approach for monitoring lava flow hazards during the 2017 Etna eruption","volume":"61","author":"Cappello","year":"2018","journal-title":"Ann. Geophys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.pce.2008.07.015","article-title":"Time domain analysis of robust satellite techniques (RST) for near real-time monitoring of active volcanoes and thermal precursor identification","volume":"34","author":"Pergola","year":"2009","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1144\/SP426.5","article-title":"Enhanced volcanic hot-spot detection using MODIS IR data: Results from the MIROVA system","volume":"426","author":"Coppola","year":"2016","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vicari, A., Bilotta, G., Bonfiglio, S., Cappello, A., Ganci, G., H\u00e8rault, A., Rustico, E., Gallo, G., and Del Negro, C. (2011). LAV@ HAZARD: A web-GIS interface for volcanic hazard assessment. Ann. Geophys., 54.","DOI":"10.4401\/ag-5347"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ganci, G., Harris, A.J., Del Negro, C., Gu\u00e9henneux, Y., Cappello, A., Labazuy, P., Calvari, S., and Gouhier, M. (2012). A year of lava fountaining at Etna: Volumes from SEVIRI. Geophys. Res. Lett., 39.","DOI":"10.1029\/2012GL051026"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2011.12.021","article-title":"An emergent strategy for volcano hazard assessment: From thermal satellite monitoring to lava flow modeling","volume":"119","author":"Ganci","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"752","article-title":"Quantifying lava flow hazards in response to effusive eruption","volume":"28","author":"Cappello","year":"2016","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1144\/SP380.10","article-title":"Review of the utility of infrared remote sensing for detecting and monitoring volcanic activity with the case study of shortwave infrared data for Lascar Volcano from 2001\u20132005","volume":"380","author":"Blackett","year":"2013","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s00445-015-0989-9","article-title":"Lava flow mapping and volume calculations for the 2012\u20132013 Tolbachik, Kamchatka, fissure eruption using bistatic TanDEM-X InSAR","volume":"77","author":"Kubanek","year":"2015","journal-title":"Bull. Volcanol."},{"key":"ref_16","first-page":"L24307","article-title":"Dynamics of a lava fountain revealed by geophysical, geochemical and thermal satellite measurements: The case of the 10 April 2011 Mt","volume":"38","author":"Bonaccorso","year":"2011","journal-title":"Etna eruption. Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1029\/2005JD006318","article-title":"Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release","volume":"110","author":"Wooster","year":"2005","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0034-4257(03)00070-1","article-title":"Fire radiative energy for quantitative study of biomass burning: Derivation from the BIRD experimental satellite and comparison to MODIS fire products","volume":"86","author":"Wooster","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17190","DOI":"10.3390\/rs71215876","article-title":"Satellite and ground based thermal observation of the 2014 effusive eruption at Stromboli volcano","volume":"7","author":"Hort","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5679","DOI":"10.1002\/2016JB013191","article-title":"Mass discharge rate retrieval combining weather radar and thermal camera observations","volume":"121","author":"Vulpiani","year":"2016","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1144\/SP426.30","article-title":"Monitoring an effusive eruption at Piton de la Fournaise using radar and thermal infrared remote sensing data: Insights into the October 2010 eruption and its lava flows","volume":"426","author":"Bato","year":"2016","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_22","first-page":"102369","article-title":"Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data","volume":"102","author":"Bisson","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.1002\/grl.50692","article-title":"New data from borehole strainmeters to infer lava fountain sources (Etna 2011\u20132012)","volume":"40","author":"Bonaccorso","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3389\/feart.2018.00083","article-title":"Mapping volcanic deposits of the 2011\u20132015 Etna eruptive events using satellite remote sensing","volume":"6","author":"Ganci","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14967","DOI":"10.3390\/rs71114967","article-title":"Quantifying effusion rates at active volcanoes through integrated time-lapse laser scanning and photography","volume":"7","author":"Slatcher","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Blackett, M. (2017). An overview of infrared remote sensing of volcanic activity. J. Imaging, 3.","DOI":"10.3390\/jimaging3020013"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1109\/JSTARS.2020.2968044","article-title":"Wavelength Calibration Correction Technique for Improved Emissivity Retrieval","volume":"13","author":"Pieper","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Marchese, F., Genzano, N., Neri, M., Falconieri, A., Mazzeo, G., and Pergola, N. (2019). A multi-channel algorithm for mapping volcanic thermal anomalies by means of Sentinel-2 MSI and Landsat-8 OLI data. Remote Sens., 11.","DOI":"10.3390\/rs11232876"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Genzano, N., Pergola, N., and Marchese, F. (2020). A Google Earth Engine tool to investigate, map and monitor volcanic thermal anomalies at global scale by means of mid-high spatial resolution satellite data. Remote Sens., 12.","DOI":"10.3390\/rs12193232"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Corradino, C., Bilotta, G., Cappello, A., Fortuna, L., and Del Negro, C. (2021). Combining Radar and Optical Satellite Imagery with Machine Learning to Map Lava Flows at Mount Etna and Fogo Island. Energies, 14.","DOI":"10.3390\/en14010197"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79261-7","article-title":"The short life of the volcanic island New Late\u2019iki (Tonga) analyzed by multi-sensor remote sensing data","volume":"10","author":"Plank","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Marchese, F., Filizzola, C., Lacava, T., Falconieri, A., Faruolo, M., Genzano, N., Mazzeo, G., Pietrapertosa, C., Pergola, N., and Tramutoli, V. (2022). Correction: Marchese et al. Mt. Etna Paroxysms of February\u2013April 2021 Monitored and Quantified through a Multi-Platform Satellite Observing System. Remote Sens. 2021, 13, 3074. Remote Sens., 14.","DOI":"10.3390\/rs14122746"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tramutoli, V., Filizzola, C., Genzano, N., and Lisi, M. (2018). Robust satellite techniques for detecting preseismic thermal anomalies. Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies, American Geophysical Union.","DOI":"10.1002\/9781119156949.ch14"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s00445-011-0487-7","article-title":"A review of algorithms for detecting volcanic hot spots in satellite infrared data","volume":"73","author":"Steffke","year":"2011","journal-title":"Bull. Volcanol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.5194\/nhess-18-1013-2018","article-title":"Pre-seismic anomalies from optical satellite observations: A review","volume":"18","author":"Jiao","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jvolgeores.2003.12.008","article-title":"MODVOLC: Near-real-time thermal monitoring of global volcanism","volume":"135","author":"Wright","year":"2004","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/S0098-3004(97)00039-3","article-title":"VAST: A program to locate and analyse volcanic thermal anomalies automatically from remotely sensed data","volume":"23","author":"Higgins","year":"1997","journal-title":"Comput. Geosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2016.02.054","article-title":"The collection 6 MODIS active fire detection algorithm and fire products","volume":"178","author":"Giglio","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2013.11.010","article-title":"Calculating radiant flux from thermally mixed pixels using a spectral library","volume":"142","author":"Murphy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11676-016-0361-8","article-title":"The progress of operational forest fire monitoring with infrared remote sensing","volume":"28","author":"Hua","year":"2017","journal-title":"J. For. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2016.02.027","article-title":"HOTMAP: Global hot target detection at moderate spatial resolution","volume":"177","author":"Murphy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Layana, S., Aguilera, F., Rojo, G., Vergara, \u00c1., Salazar, P., Quispe, J., Urra, P., and Urrutia, D. (2020). Volcanic Anomalies monitoring System (VOLCANOMS), a low-cost volcanic monitoring system based on Landsat images. Remote Sens., 12.","DOI":"10.3390\/rs12101589"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Massimetti, F., Coppola, D., Laiolo, M., Valade, S., Cigolini, C., and Ripepe, M. (2020). Volcanic hot-spot detection using SENTINEL-2: A comparison with MODIS\u2013MIROVA thermal data series. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-5095"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Corradino, C., Amato, E., Torrisi, F., and Del Negro, C. (October, January 29). Towards an automatic generalized machine learning approach to map lava flows. Proceedings of the 2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), Catania, Italy.","DOI":"10.1109\/CNNA49188.2021.9610813"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Amato, E., Corradino, C., Torrisi, F., and Del Negro, C. (2021, January 7\u20138). Mapping lava flows at Etna Volcano using Google Earth Engine, open-access satellite data, and machine learning. Proceedings of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius.","DOI":"10.1109\/ICECCME52200.2021.9591110"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6592","DOI":"10.1029\/2018JB015911","article-title":"Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data","volume":"123","author":"Anantrasirichai","year":"2018","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2011.06.020","article-title":"Development of a machine learning technique for automatic analysis of seafloor image data: Case example, Pogonophora coverage at mud volcanoes","volume":"39","author":"Jerosch","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Corradino, C., Ganci, G., Cappello, A., Bilotta, G., Calvari, S., and Del Negro, C. (2020). Recognizing Eruptions of Mount Etna through Machine Learning using Multiperspective Infrared Images. Remote Sens., 12.","DOI":"10.3390\/rs12060970"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, M., Yang, H., Jin, Y., Zhang, X., and Liu, H. (2021). Mapping regional soil organic matter based on sentinel-2a and modis imagery using machine learning algorithms and google earth engine. Remote Sens., 13.","DOI":"10.3390\/rs13152934"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_53","unstructured":"Bonaccorso, G. (2017). Machine Learning Algorithms, Packt Publishing Ltd."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/1536867X20909688","article-title":"The random forest algorithm for statistical learning","volume":"20","author":"Schonlau","year":"2020","journal-title":"Stata J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4012","DOI":"10.1109\/TIP.2018.2834830","article-title":"Improved random forest for classification","volume":"27","author":"Paul","year":"2018","journal-title":"IEEE Trans. Image Processing"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1126\/science.abm9423","article-title":"Reactivation of Cumbre Vieja volcano","volume":"374","year":"2021","journal-title":"Science"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1111\/gto.12388","article-title":"The 2021 eruption of the Cumbre Vieja Volcanic Ridge on La Palma, Canary Islands","volume":"38","author":"Carracedo","year":"2022","journal-title":"Geol. Today"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Eibl, E.P., Thordarson, T., H\u00f6skuldsson, \u00c1., Gudnason, E.\u00c1., Dietrich, T., Hersir, G.P., and \u00c1g\u00fastsd\u00f3ttir, T. (2022). Evolving Shallow-conduit Container Affects the Lava Fountaining during the 2021 Fagradalsfjall Eruption, Iceland. Res. Sq.","DOI":"10.21203\/rs.3.rs-1453738\/v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e2021GL097125","DOI":"10.1029\/2021GL097125","article-title":"Volume, effusion rate, and lava transport during the 2021 Fagradalsfjall eruption: Results from near real-time photogrammetric monitoring","volume":"49","author":"Pedersen","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Calvari, S., Di Traglia, F., Ganci, G., Giudicepietro, F., Macedonio, G., Cappello, A., Nolesini, T., Pecora, E., Bilotta, G., and Centorrino, V. (2020). Overflows and pyroclastic density currents in March-April 2020 at Stromboli volcano detected by remote sensing and seismic monitoring data. Remote Sens., 12.","DOI":"10.3390\/rs12183010"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Corradino, C., Amato, E., Torrisi, F., Calvari, S., and Del Negro, C. (2021). Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space. Remote Sens., 13.","DOI":"10.3390\/rs13204080"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.epsl.2010.03.040","article-title":"A model of degassing for Stromboli volcano","volume":"295","author":"Aiuppa","year":"2010","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_64","first-page":"1","article-title":"A 50 yr eruption of a basaltic composite cone: Pacaya, Guatemala","volume":"498","author":"Rose","year":"2013","journal-title":"Geol. Soc. Am. Spec. Pap."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3390\/rs8010073","article-title":"Post-eruption deformation processes measured using ALOS-1 and UAVSAR InSAR at Pacaya Volcano, Guatemala","volume":"8","author":"Schaefer","year":"2016","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ganci, G., Cappello, A., Zago, V., Bilotta, G., Herault, A., and Del Negro, C. (2018). 3D Lava flow mapping of the 17\u201325 May 2016 Etna eruption using tri-stereo optical satellite data. Ann. Geophys., 62.","DOI":"10.4401\/ag-7875"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1007\/s00445-013-0690-9","article-title":"From source to surface: Dynamics of Etna\u2019s lava fountains investigated by continuous strain, magnetic, ground and satellite thermal data","volume":"75","author":"Bonaccorso","year":"2013","journal-title":"Bull. Volcanol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Marchese, F., Filizzola, C., Lacava, T., Falconieri, A., Faruolo, M., Genzano, N., Mazzeo, G., Pietrapertosa, C., Pergola, N., and Tramutoli, V. (2021). Etna paroxysms of February\u2013April 2021 monitored and quantified through a multi-platform satellite observing system. Remote Sens., 13.","DOI":"10.3390\/rs13163074"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Calvari, S., Bonaccorso, A., and Ganci, G. (2021). Anatomy of a Paroxysmal Lava Fountain at Etna Volcano: The Case of the 12 March 2021, Episode. Remote Sens., 13.","DOI":"10.3390\/rs13153052"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Torrisi, F., Folzani, F., Corradino, C., Amato, E., and Del Negro, C. (2021). Detecting Volcanic Ash Plume Components from Space using Machine Learning Techniques. Earth Space Sci. Open Arch., 1.","DOI":"10.1002\/essoar.10509947.1"},{"key":"ref_71","unstructured":"Torrisi, F. (2022). Automatic approach to detect volcanic plumes using SEVIRI data and machine learning techniques. Il Nuovo Cim. 45 C, 81."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_73","first-page":"142","article-title":"Spectral properties of volcanic materials from hyperspectral field and satellite data compared with LiDAR data at Mt","volume":"11","author":"Spinetti","year":"2009","journal-title":"Etna. Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/19475705.2012.680503","article-title":"Mapping lava flows from Nyamuragira volcano (1967\u20132011) with satellite data and automated classification methods","volume":"4","author":"Head","year":"2013","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Corradino, C., Ganci, G., Cappello, A., Bilotta, G., H\u00e9rault, A., and Del Negro, C. (2019). Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques. Remote Sens., 11.","DOI":"10.3390\/rs11161916"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jvolgeores.2017.07.014","article-title":"Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image","volume":"345","author":"Li","year":"2017","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2004.03.015","article-title":"Mapping recent lava flows at Westdahl Volcano, Alaska, using radar and optical satellite imagery","volume":"91","author":"Lu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1016\/j.rse.2008.03.003","article-title":"Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)","volume":"112","author":"Giglio","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Random forests. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2747\/1548-1603.49.5.623","article-title":"An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA","volume":"49","author":"Ghimire","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2018.11.001","article-title":"Influence of topographic data uncertainties and model resolution on the numerical simulation of lava flows","volume":"112","author":"Bilotta","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1002\/2015JB012666","article-title":"Lava flow hazard modeling during the 2014-2015 Fogo eruption, Cape Verde","volume":"121","author":"Cappello","year":"2016","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s00445-014-0879-6","article-title":"Numerical simulation of basaltic lava flows in the Auckland Volcanic Field, New Zealand\u2014Implication for volcanic hazard assessment","volume":"76","author":"Kereszturi","year":"2014","journal-title":"Bull. Volcanol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jvolgeores.2015.11.002","article-title":"Emplacement conditions of the 1256 AD Al-Madinah lava flow field in Harrat Rahat, Kingdom of Saudi Arabia\u2014Insights from surface morphology and lava flow simulations","volume":"309","author":"Kereszturi","year":"2016","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Rogers, J., and Gunn, S. (2005). Identifying feature relevance using a random forest. International Statistical and Optimization Perspectives Workshop. In Subspace, Latent Structure and Feature Selection, Springer.","DOI":"10.1007\/11752790_12"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:38Z","timestamp":1760142158000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":87,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174370"],"URL":"https:\/\/doi.org\/10.3390\/rs14174370","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]}}}