Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this con...
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ftingv:oai:www.earth-prints.org:2122/15847 2023-05-15T16:09:41+02:00 Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case Petracca, Ilaria de Santis, Davide Picchiani, Matteo Corradini, Stefano Guerrieri, Lorenzo Prata, Fred Merucci, Luca Stelitano, Dario Del Frate, Fabio Salvucci, Giorgia Schiavon, Giovanni #PLACEHOLDER_PARENT_METADATA_VALUE# Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione ONT, Roma, Italia 2022-12-14 http://hdl.handle.net/2122/15847 https://doi.org/10.5194/amt-15-7195-2022 en eng Egu-Copernicus Atmospheric Measurement Techniques /15 (2022) 1867-1381 http://hdl.handle.net/2122/15847 doi:10.5194/amt-15-7195-2022 open article 2022 ftingv https://doi.org/10.5194/amt-15-7195-2022 2023-01-03T23:26:42Z Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects. Published 7195–7210 5V. Processi eruttivi e post-eruttivi ... Article in Journal/Newspaper Eyjafjallajökull Earth-Prints (Istituto Nazionale di Geofisica e Vulcanologia) Atmospheric Measurement Techniques 15 24 7195 7210 |
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Open Polar |
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Earth-Prints (Istituto Nazionale di Geofisica e Vulcanologia) |
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ftingv |
language |
English |
description |
Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects. Published 7195–7210 5V. Processi eruttivi e post-eruttivi ... |
author2 |
#PLACEHOLDER_PARENT_METADATA_VALUE# Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione ONT, Roma, Italia |
format |
Article in Journal/Newspaper |
author |
Petracca, Ilaria de Santis, Davide Picchiani, Matteo Corradini, Stefano Guerrieri, Lorenzo Prata, Fred Merucci, Luca Stelitano, Dario Del Frate, Fabio Salvucci, Giorgia Schiavon, Giovanni |
spellingShingle |
Petracca, Ilaria de Santis, Davide Picchiani, Matteo Corradini, Stefano Guerrieri, Lorenzo Prata, Fred Merucci, Luca Stelitano, Dario Del Frate, Fabio Salvucci, Giorgia Schiavon, Giovanni Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
author_facet |
Petracca, Ilaria de Santis, Davide Picchiani, Matteo Corradini, Stefano Guerrieri, Lorenzo Prata, Fred Merucci, Luca Stelitano, Dario Del Frate, Fabio Salvucci, Giorgia Schiavon, Giovanni |
author_sort |
Petracca, Ilaria |
title |
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
title_short |
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
title_full |
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
title_fullStr |
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
title_full_unstemmed |
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case |
title_sort |
volcanic cloud detection using sentinel-3 satellite data by means of neural networks: the raikoke 2019 eruption test case |
publisher |
Egu-Copernicus |
publishDate |
2022 |
url |
http://hdl.handle.net/2122/15847 https://doi.org/10.5194/amt-15-7195-2022 |
genre |
Eyjafjallajökull |
genre_facet |
Eyjafjallajökull |
op_relation |
Atmospheric Measurement Techniques /15 (2022) 1867-1381 http://hdl.handle.net/2122/15847 doi:10.5194/amt-15-7195-2022 |
op_rights |
open |
op_doi |
https://doi.org/10.5194/amt-15-7195-2022 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
15 |
container_issue |
24 |
container_start_page |
7195 |
op_container_end_page |
7210 |
_version_ |
1766405523720110080 |